Articles | Volume 16, issue 21
Atmos. Chem. Phys., 16, 13465–13475, 2016
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.
Atmos. Chem. Phys., 16, 13465–13475, 2016
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 et al.
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- Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors L. Frederickson et al. 10.3390/atmos11070749
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- Exploring the Potential of Using Carbonyl Sulfide to Track the Urban Biosphere Signal G. Villalba et al. 10.1029/2020JD034106
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- A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning K. Mao et al. 10.1016/j.comcom.2021.08.003
- The Berkeley Environmental Air-quality and CO<sub>2</sub> Network: field calibrations of sensor temperature dependence and assessment of network scale CO<sub>2</sub> accuracy E. Delaria et al. 10.5194/amt-14-5487-2021
- Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System J. Ware et al. 10.1029/2018JD029224
- The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability B. Nathan et al. 10.5194/acp-21-14089-2021
- Modeling Ozone in the Eastern U.S. using a Fuel-Based Mobile Source Emissions Inventory B. McDonald et al. 10.1021/acs.est.8b00778
- Social Impact Assessment of HealthyAIR Tool for Real-Time Detection of Pollution Risk A. Moreno Cano et al. 10.3390/su12239856
- 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
- Net CO<sub>2</sub> fossil fuel emissions of Tokyo estimated directly from measurements of the Tsukuba TCCON site and radiosondes A. Babenhauserheide et al. 10.5194/amt-13-2697-2020
- Performance evaluation of light weight gas sensor system suitable for airborne applications against co-location gas analysers over Delhi A. Ahlawat et al. 10.1016/j.scitotenv.2019.134016
- Observing local CO<sub>2</sub> sources using low-cost, near-surface urban monitors A. Shusterman et al. 10.5194/acp-18-13773-2018
- Tall tower eddy covariance measurements of CO2 fluxes in Vienna, Austria B. Matthews & H. Schume 10.1016/j.atmosenv.2022.118941
- 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. 10.1007/s10661-017-5829-2
- Assessing Lagrangian inverse modelling of urban anthropogenic CO2 fluxes using in situ aircraft and ground-based measurements in the Tokyo area I. Pisso et al. 10.1186/s13021-019-0118-8
- Top‐Down Constraints on Anthropogenic CO 2 Emissions Within an Agricultural‐Urban Landscape C. Hu et al. 10.1029/2017JD027881
- Characterization of a commercial lower-cost medium-precision non-dispersive infrared sensor for atmospheric CO<sub>2</sub> monitoring in urban areas E. Arzoumanian et al. 10.5194/amt-12-2665-2019
- Detection of fossil fuel emission trends in the presence of natural carbon cycle variability Y. Yin et al. 10.1088/1748-9326/ab2dd7
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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. 10.5194/acp-18-8265-2018
- Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties K. Wu et al. 10.1525/elementa.138
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Latest update: 08 Aug 2022
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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