Articles | Volume 16, issue 21
https://doi.org/10.5194/acp-16-13465-2016
© Author(s) 2016. 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-16-13465-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Nov 2016
Research article |
| 01 Nov 2016
Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density
Alexander J. Turner
School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
Environmental Energy and Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Alexis A. Shusterman
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
Brian C. McDonald
Department of Civil and Engineering, University of California at Berkeley, Berkeley, CA, USA
now at: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Virginia Teige
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
Robert A. Harley
Department of Civil and Engineering, University of California at Berkeley, Berkeley, CA, USA
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
Department of Earth and Planetary Sciences, University of California at Berkeley, Berkeley, CA, USA
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- Designing an Atmospheric Monitoring Network to Verify National CO2 Emissions S. Sim et al. https://doi.org/10.1007/s13143-023-00343-3
- ACROPOLIS: Munich urban CO2 sensor network P. Aigner et al. https://doi.org/10.5194/amt-19-745-2026
- Observing local CO2 sources using low-cost, near-surface urban monitors A. Shusterman et al. https://doi.org/10.5194/acp-18-13773-2018
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- Monitoring urban carbon dioxide emissions from the atmosphere: insights from vertical tower observations in Beijing, China K. Liu et al. https://doi.org/10.1016/j.atmosenv.2026.122166
- Local-scale inversion of agricultural ammonia emissions: a case study on Schiermonnikoog, the Netherlands S. Li et al. https://doi.org/10.5194/acp-25-15593-2025
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- Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform D. Mallia et al. https://doi.org/10.1021/acs.est.0c04388
- Improving data reliability: A quality control practice for low-cost PM2.5 sensor network X. Qiao et al. https://doi.org/10.1016/j.scitotenv.2021.146381
- Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT L. Kunik et al. https://doi.org/10.1525/elementa.375
- Designing urban CO2 sensor networks under realistic uncertainties and biases: Results from synthetic studies in Berlin and Munich C. Lüken-Winkels et al. https://doi.org/10.1525/elementa.2025.00118
- Observing Annual Trends in Vehicular CO2 Emissions J. Kim et al. https://doi.org/10.1021/acs.est.1c06828
- Background Characteristics and Influence Analysis of Greenhouse Gases at Jinsha Atmospheric Background Station in China D. Wu et al. https://doi.org/10.3390/atmos14101541
- Mobile Observations of Intracity Variations in Atmospheric CO2 and CH4 J. Wang et al. https://doi.org/10.1007/s00376-025-5059-4
- Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network M. Patel et al. https://doi.org/10.1021/acsestair.5c00069
- Toward Establishing a Low-cost UAV Coordinated Carbon Observation Network (LUCCN): First Integrated Campaign in China D. Yang et al. https://doi.org/10.1007/s00376-023-3107-5
- Deliberating performance targets workshop: Potential paths for emerging PM2.5 and O3 air sensor progress R. Williams et al. https://doi.org/10.1016/j.aeaoa.2019.100031
- A global map of emission clumps for future monitoring of fossil fuel CO2 emissions from space Y. Wang et al. https://doi.org/10.5194/essd-11-687-2019
- Simulating out-of-sample atmospheric transport to enable flux inversions N. Dadheech & A. Turner https://doi.org/10.5194/acp-26-427-2026
- 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. https://doi.org/10.5194/acp-21-10015-2021
- A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning K. Mao et al. https://doi.org/10.1016/j.comcom.2021.08.003
- The Berkeley Environmental Air-quality and CO2 Network: field calibrations of sensor temperature dependence and assessment of network scale CO2 accuracy E. Delaria et al. https://doi.org/10.5194/amt-14-5487-2021
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- Modeling Ozone in the Eastern U.S. using a Fuel-Based Mobile Source Emissions Inventory B. McDonald et al. https://doi.org/10.1021/acs.est.8b00778
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- Southern California megacity CO2, CH4, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model J. Hedelius et al. https://doi.org/10.5194/acp-18-16271-2018
- Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use Q. Cai et al. https://doi.org/10.3390/s24092680
- Using Horizontal LiDAR Scans to Capture Net CO2 Emissions at Intra-Urban Spatial Granularity: A Review R. Dubey et al. https://doi.org/10.3390/urbansci10080439
- Net CO2 fossil fuel emissions of Tokyo estimated directly from measurements of the Tsukuba TCCON site and radiosondes A. Babenhauserheide et al. https://doi.org/10.5194/amt-13-2697-2020
- Low-cost urban carbon monitoring network and implications for china: a comprehensive review H. Jiang et al. https://doi.org/10.1007/s11356-023-29836-4
- Designing an Atmospheric Monitoring Network to Verify National CO2 Emissions S. Sim et al. https://doi.org/10.1007/s13143-023-00343-3
- ACROPOLIS: Munich urban CO2 sensor network P. Aigner et al. https://doi.org/10.5194/amt-19-745-2026
- Observing local CO2 sources using low-cost, near-surface urban monitors A. Shusterman et al. https://doi.org/10.5194/acp-18-13773-2018
- Tall tower eddy covariance measurements of CO2 fluxes in Vienna, Austria B. Matthews & H. Schume https://doi.org/10.1016/j.atmosenv.2022.118941
- Source decomposition of eddy-covariance CO2 flux measurements for evaluating a high-resolution urban CO2 emissions inventory K. Wu et al. https://doi.org/10.1088/1748-9326/ac7c29
- A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks H. Xu et al. https://doi.org/10.3390/su16103998
- Inter-seasonal and spatial distribution of ground-level greenhouse gases (CO2, CH4, N2O) over Nagpur in India and their management roadmap D. Majumdar et al. https://doi.org/10.1007/s10661-017-5829-2
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- High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport N. Dadheech et al. https://doi.org/10.5194/acp-25-5159-2025
- Networked Low-Cost Sensor Systems for Urban Air Quality Monitoring: A Long-Term Use-Case in Bari (Italy) M. Penza et al. https://doi.org/10.3390/chemosensors13110380
- Wind-constrained diagnosis of 500 m urban CO2 emission spatial proxies using PAS observations and WRF-Chem simulations: a Hefei case study S. Wu et al. https://doi.org/10.3389/fenvs.2026.1929267
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO2 monitoring in urban areas E. Arzoumanian et al. https://doi.org/10.5194/amt-12-2665-2019
- Detection of fossil fuel emission trends in the presence of natural carbon cycle variability Y. Yin et al. https://doi.org/10.1088/1748-9326/ab2dd7
- A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. https://doi.org/10.3389/fenvs.2024.1394281
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- Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales A. Turner et al. https://doi.org/10.5194/acp-18-8265-2018
- Monitoring Greenhouse Gases in the Open Atmosphere by the Fourier Spectroscopy Method I. Golyak et al. https://doi.org/10.1134/S1990793123020264
- Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model X. Zhao et al. https://doi.org/10.5194/acp-23-14325-2023
- Мониторинг парниковых газов в открытой атмосфере методом фурье-спектроскопии И. Голяк et al. https://doi.org/10.31857/S0207401X23040088
- Open-data-based city-scale gridded carbon dioxide emission inventory: supporting urban carbon monitoring in Chengdu, China R. Wang et al. https://doi.org/10.20517/cf.2024.19
- Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris J. Lian et al. https://doi.org/10.5194/amt-17-5821-2024
- Bayesian inversion of urban sectoral scale anthropogenic CO 2 emissions coupled with weather research and forecasting (WRF) and stochastic time-inverted lagrangian transport (STILT) models in Chengdu-Chongqing economic circle Z. Xia et al. https://doi.org/10.1080/17583004.2026.2627657
- Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL S. Vardag & R. Maiwald https://doi.org/10.5194/gmd-17-1885-2024
- Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions K. Wu et al. https://doi.org/10.3390/rs15204904
- Atmospheric anthropogenic CO2 variations observed by tower in-situ measurements and simulated by the STILT model in the Beijing megacity region H. Yang et al. https://doi.org/10.1016/j.atmosres.2025.108258
- Multi-year observations of variable incomplete combustion in the New York megacity L. Schiferl et al. https://doi.org/10.5194/acp-24-10129-2024
- Site selection and effects of background towers on urban CO2 estimates: A case study from central downtown Zhengzhou in China G. Ren et al. https://doi.org/10.1016/j.envres.2024.120169
- Working towards confident spaceborne monitoring of carbon emissions from cities using Orbiting Carbon Observatory-2 L. Labzovskii et al. https://doi.org/10.1016/j.rse.2019.111359
- A 30-month field evaluation of low-cost CO2 sensors using a reference instrument Q. Cai et al. https://doi.org/10.5194/amt-18-4871-2025
- Effects of Environmental Factors on the Performance of Ground-Based Low-Cost CO2 Sensors X. Ren et al. https://doi.org/10.3390/s25196114
- Anthropogenic CO2 emissions from a megacity in the Yangtze River Delta of China C. Hu et al. https://doi.org/10.1007/s11356-018-2325-3
- Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors L. Frederickson et al. https://doi.org/10.3390/atmos11070749
- Exploring the Potential of Using Carbonyl Sulfide to Track the Urban Biosphere Signal G. Villalba et al. https://doi.org/10.1029/2020JD034106
- Assessing vehicle fuel efficiency using a dense network of CO2 observations H. Fitzmaurice et al. https://doi.org/10.5194/acp-22-3891-2022
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Saved (final revised paper)
Latest update: 22 Oct 2026
Short summary
Our paper investigates the ability of different types of observational networks to estimate urban CO2 emissions. We have quantified the trade-off between precision and network density for estimating urban greenhouse gas emissions. Our results show that different observing systems may fall into noise- or site-limited regimes where reducing the uncertainty in the estimated emissions is governed by a single factor.
Our paper investigates the ability of different types of observational networks to estimate...
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