Articles | Volume 18, issue 7
https://doi.org/10.5194/acp-18-4765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-4765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2018
Research article |
| 09 Apr 2018
| 09 Apr 2018
Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling
Alecia Nickless
CORRESPONDING AUTHOR
Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South Africa
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK
Peter J. Rayner
School of Earth Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
Francois Engelbrecht
CSIR Natural Resources and the Environment – Climate Studies, Modelling and Environmental Health, P.O. Box 395, Pretoria, 0001, South Africa
Unit for Environmental Sciences and Management, North-West University, Potchefstroom, 2520, South Africa
Ernst-Günther Brunke
South African Weather Service c/o CSIR, P.O. Box 320, Stellenbosch, 7599, South Africa
Birgit Erni
Department of Statistical Sciences, University of Cape Town, Cape Town, 7701, South Africa
Centre for Statistics in Ecology, the Environment and Conservation, University of Cape Town, Cape Town, 7701, South Africa
Robert J. Scholes
Global Change Institute, University of the Witwatersrand, Johannesburg, 2050, South Africa
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20 citations as recorded by crossref.
- Urbanization and carbon emission: causality evidence from the new industrialized economies K. Khan et al. 10.1007/s10668-019-00479-1
- Background conditions for an urban greenhouse gas network in the Washington, DC, and Baltimore metropolitan region A. Karion et al. 10.5194/acp-21-6257-2021
- 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
- High‐Resolution Lagrangian Inverse Modeling of CO2 Emissions Over the Paris Region During the First 2020 Lockdown Period K. Nalini et al. 10.1029/2021JD036032
- Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform D. Mallia et al. 10.1021/acs.est.0c04388
- Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area Q. Wang et al. 10.3390/rs13112037
- Development of a Climate Forcing Observation System for Africa: Data-Related Considerations J. Beck et al. 10.5334/dsj-2019-042
- An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
- Assessing local emission for air pollution via data experiments Y. Zhu et al. 10.1016/j.atmosenv.2021.118323
- Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories Y. Hu & Y. Shi 10.3390/atmos12070811
- Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations T. Zheng et al. 10.1088/1748-9326/ab25ae
- An emerging GHG estimation approach can help cities achieve their climate and sustainability goals K. Mueller et al. 10.1088/1748-9326/ac0f25
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. 10.1021/acs.est.3c09642
- Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System B. Nathan et al. 10.1029/2018JD029231
- Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities? D. Mallia et al. 10.1029/2023JD038686
- Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
- The influence of near-field fluxes on seasonal carbon dioxide enhancements: results from the Indianapolis Flux Experiment (INFLUX) N. Miles et al. 10.1186/s13021-020-00166-z
- Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations H. Kim et al. 10.5194/acp-20-10259-2020
- High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India S. Sijikumar et al. 10.1016/j.atmosenv.2023.119868
- Comparing different methods for statistical modeling of particulate matter in Tehran, Iran V. Mehdipour et al. 10.1007/s11869-018-0615-z
19 citations as recorded by crossref.
- Urbanization and carbon emission: causality evidence from the new industrialized economies K. Khan et al. 10.1007/s10668-019-00479-1
- Background conditions for an urban greenhouse gas network in the Washington, DC, and Baltimore metropolitan region A. Karion et al. 10.5194/acp-21-6257-2021
- 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
- High‐Resolution Lagrangian Inverse Modeling of CO2 Emissions Over the Paris Region During the First 2020 Lockdown Period K. Nalini et al. 10.1029/2021JD036032
- Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform D. Mallia et al. 10.1021/acs.est.0c04388
- Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area Q. Wang et al. 10.3390/rs13112037
- Development of a Climate Forcing Observation System for Africa: Data-Related Considerations J. Beck et al. 10.5334/dsj-2019-042
- An atmospheric inversion over the city of Cape Town: sensitivity analyses A. Nickless et al. 10.5194/acp-19-7789-2019
- Assessing local emission for air pollution via data experiments Y. Zhu et al. 10.1016/j.atmosenv.2021.118323
- Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories Y. Hu & Y. Shi 10.3390/atmos12070811
- Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations T. Zheng et al. 10.1088/1748-9326/ab25ae
- An emerging GHG estimation approach can help cities achieve their climate and sustainability goals K. Mueller et al. 10.1088/1748-9326/ac0f25
- Sustained Reductions of Bay Area CO2 Emissions 2018–2022 N. Asimow et al. 10.1021/acs.est.3c09642
- Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System B. Nathan et al. 10.1029/2018JD029231
- Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities? D. Mallia et al. 10.1029/2023JD038686
- Greenhouse gas observation network design for Africa A. Nickless et al. 10.1080/16000889.2020.1824486
- The influence of near-field fluxes on seasonal carbon dioxide enhancements: results from the Indianapolis Flux Experiment (INFLUX) N. Miles et al. 10.1186/s13021-020-00166-z
- Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations H. Kim et al. 10.5194/acp-20-10259-2020
- High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India S. Sijikumar et al. 10.1016/j.atmosenv.2023.119868
1 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 18 Apr 2024
Short summary
Carbon dioxide emissions and uptake were estimated for Cape Town, South Africa. We placed two high-precision analysers in lighthouses located on either end of Cape Town (Robben Island and Hangklip). The Cape Point GAW station provided background measurements. We were able to improve the agreement between modelled and observed concentrations, relative to initial estimates provided. This methodology could potentially be scaled up to provide monitoring and verification of city emissions.
Carbon dioxide emissions and uptake were estimated for Cape Town, South Africa. We placed two...
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