Articles | Volume 17, issue 21 
            
                
                    
            
            
            https://doi.org/10.5194/acp-17-13297-2017
                    © Author(s) 2017. This work is distributed under 
the Creative Commons Attribution 3.0 License.
                the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-13297-2017
                    © Author(s) 2017. This work is distributed under 
the Creative Commons Attribution 3.0 License.
                the Creative Commons Attribution 3.0 License.
A multi-model approach to monitor emissions of CO2 and CO from an urban–industrial complex
                                            Meteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
                                        
                                    
                                            Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
                                        
                                    Hugo A. C. Denier van der Gon
                                            Department of Climate, Air and Sustainability, TNO, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
                                        
                                    Michiel K. van der Molen
                                            Meteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
                                        
                                    Hendrika A. M. Sterk
                                            National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, the Netherlands
                                        
                                    Arjan Hensen
                                            Energy research Centre of the Netherlands, P.O. Box 1, 1755 ZG Petten, the Netherlands
                                        
                                    Wouter Peters
                                            Meteorology and Air Quality Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
                                        
                                    
                                            Centre for Isotope Research, Energy and  Sustainability Research Institute Groningen,
University of Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands
                                        
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                            Cited
15 citations as recorded by crossref.
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. 10.1088/1748-9326/ad0f74
 - Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2 I. Super et al. 10.5194/gmd-13-2695-2020
 - Time-delayed causal network analysis of meteorological variables and air pollutants in Baguio city M. Liponhay et al. 10.1016/j.apr.2024.102095
 - Quantifying the Impact of the COVID-19 Pandemic Restrictions on CO, CO2, and CH4 in Downtown Toronto Using Open-Path Fourier Transform Spectroscopy Y. You et al. 10.3390/atmos12070848
 - Diurnal and Seasonal Variation of Area-Fugitive Methane Advective Flux from an Open-Pit Mining Facility in Northern Canada Using WRF M. Nambiar et al. 10.3390/atmos11111227
 - Anthropogenic and natural controls on atmospheric δ13C-CO2 variations in the Yangtze River delta: insights from a carbon isotope modeling framework C. Hu et al. 10.5194/acp-21-10015-2021
 - Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design I. Super et al. 10.5194/acp-20-1795-2020
 - AQI multi-point spatiotemporal prediction based on K-mean clustering and RNN-LSTM model J. Zhu et al. 10.1088/1742-6596/2006/1/012022
 - Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model X. Zhao et al. 10.5194/acp-23-14325-2023
 - Source–Receptor Relationships and Cluster Analysis of CO2, CH4, and CO Concentrations in West Africa: The Case of Lamto in Côte d’Ivoire D. Tiemoko et al. 10.3390/atmos11090903
 - Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5) I. Super et al. 10.5194/gmd-17-7263-2024
 - Synthesis of Urban CO2 Emission Estimates from Multiple Methods from the Indianapolis Flux Project (INFLUX) J. Turnbull et al. 10.1021/acs.est.8b05552
 - A newly developed Lagrangian chemical transport scheme: Part 1. Simulation of a boreal forest fire plume Y. Liu et al. 10.1016/j.scitotenv.2023.163232
 - Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation A. Karagodin-Doyennel et al. 10.5194/gmd-18-4571-2025
 - Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models F. Maier et al. 10.5194/gmd-15-5391-2022
 
15 citations as recorded by crossref.
- A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks L. Nayagam et al. 10.1088/1748-9326/ad0f74
 - Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2 I. Super et al. 10.5194/gmd-13-2695-2020
 - Time-delayed causal network analysis of meteorological variables and air pollutants in Baguio city M. Liponhay et al. 10.1016/j.apr.2024.102095
 - Quantifying the Impact of the COVID-19 Pandemic Restrictions on CO, CO2, and CH4 in Downtown Toronto Using Open-Path Fourier Transform Spectroscopy Y. You et al. 10.3390/atmos12070848
 - Diurnal and Seasonal Variation of Area-Fugitive Methane Advective Flux from an Open-Pit Mining Facility in Northern Canada Using WRF M. Nambiar et al. 10.3390/atmos11111227
 - Anthropogenic and natural controls on atmospheric δ13C-CO2 variations in the Yangtze River delta: insights from a carbon isotope modeling framework C. Hu et al. 10.5194/acp-21-10015-2021
 - Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design I. Super et al. 10.5194/acp-20-1795-2020
 - AQI multi-point spatiotemporal prediction based on K-mean clustering and RNN-LSTM model J. Zhu et al. 10.1088/1742-6596/2006/1/012022
 - Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model X. Zhao et al. 10.5194/acp-23-14325-2023
 - Source–Receptor Relationships and Cluster Analysis of CO2, CH4, and CO Concentrations in West Africa: The Case of Lamto in Côte d’Ivoire D. Tiemoko et al. 10.3390/atmos11090903
 - Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5) I. Super et al. 10.5194/gmd-17-7263-2024
 - Synthesis of Urban CO2 Emission Estimates from Multiple Methods from the Indianapolis Flux Project (INFLUX) J. Turnbull et al. 10.1021/acs.est.8b05552
 - A newly developed Lagrangian chemical transport scheme: Part 1. Simulation of a boreal forest fire plume Y. Liu et al. 10.1016/j.scitotenv.2023.163232
 - Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation A. Karagodin-Doyennel et al. 10.5194/gmd-18-4571-2025
 - Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models F. Maier et al. 10.5194/gmd-15-5391-2022
 
Latest update: 04 Nov 2025
Short summary
                    In this research we examined the use of different models to simulate CO2 concentrations in and around urban areas. We find that in the presence of large stack emissions in a gridded model is insufficient to represent the small dimensions of the CO2 plumes. A plume model improves this representation up to 10–14 km from the stack. Better model results can improve the estimate of CO2 emissions from urban areas and assist in identifying efficient emission reduction policies.
                    In this research we examined the use of different models to simulate CO2 concentrations in and...
                    
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