Preprints
https://doi.org/10.5194/egusphere-2026-3381
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-3381
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
28 Jul 2026
| 28 Jul 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Constraining anthropogenic CO2 fluxes in mixed urban source areas using eddy covariance and footprint-informed ecological parameter migration
Abstract. Urban net CO2 fluxes measured by eddy covariance (EC) cannot be treated as anthropogenic emissions without biogenic constraints, because they integrate fossil-fuel sources, biogenic processes, human respiration, and changing footprints. Existing partitioning approaches often require isotopes, tracers, multi-species fluxes, or clean vegetation/emission sectors unavailable at many urban sites. We developed a footprint-informed ecological parameter-migration framework to constrain anthropogenic CO2 fluxes (Fff) in mixed urban source areas of Guangzhou, a humid subtropical megacity. A highly vegetated, weakly disturbed suburban donor site provided ecological parameters, which were transferred to a mixed urban target site using dynamic footprints and footprint-weighted enhanced vegetation index (EVIfp). The migrated parameters produced plausible diurnal and seasonal gross primary productivity (GPP) and ecosystem respiration (Reco), supporting the donor–target constraint. At the target site, daytime biogenic uptake masked anthropogenic emissions and occasionally drove net CO2 flux toward neutral or weakly negative values. After subtracting Reco, GPP, and human respiration, Fff remained consistently positive throughout the day (1.80–5.14 μmol m−2 s−1). Fff showed morning and evening peaks consistent with NOx, CO, wind-sector source areas, and traffic indicators, while footprint-aligned inventories provided magnitude context and suggested spatial-proxy mismatch in a high-population urban functional zone with relatively low on-site combustion. Uncertainty was dominated by GPP light-response structure, with smaller effects from other perturbations. These results demonstrate the feasibility of extracting footprint-scale Fff from mixed urban source areas using conventional EC, remote-sensing, and footprint data where isotopic or multi-species flux observations are unavailable.
How to cite. Zhang, J., Liang, Y., Yang, Y., Pei, C., Huang, H., Huang, B., Lian, X., Huang, Y., Peng, Y., Cheng, C., Wu, C., Zhou, Z., and Li, M.: Constraining anthropogenic CO2 fluxes in mixed urban source areas using eddy covariance and footprint-informed ecological parameter migration, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-3381, 2026.
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Jinwen Zhang
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Yongjian Liang
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Guangzhou Ecological and Environmental Monitoring Center Station, Guangdong, Guangzhou 510006, China
Yufei Yang
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Chenglei Pei
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Guangzhou Ecological and Environmental Monitoring Center Station, Guangdong, Guangzhou 510006, China
Hebiao Huang
Guangzhou Academy of Agricultural and Rural Sciences, Guangdong, Guangzhou 510450, China
Bo Huang
Guangzhou Hexin Instrument Co., Ltd., Guangdong, Guangzhou 510530, China
Xiufeng Lian
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Yingyan Huang
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Guangzhou Ecological and Environmental Monitoring Center Station, Guangdong, Guangzhou 510006, China
Yuwen Peng
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Guangzhou Ecological and Environmental Monitoring Center Station, Guangdong, Guangzhou 510006, China
Chunlei Cheng
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Zhen Zhou
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Mei Li
CORRESPONDING AUTHOR
College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Guangdong–Hongkong–Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment, Guangdong, Guangzhou 510308, China
Short summary
Separating human-related carbon dioxide emissions from vegetation exchange in cities often requires costly measurements. We developed a more accessible approach by combining tower-based carbon dioxide flux observations, satellite vegetation data, and ecological models. We found that vegetation uptake partly masked human-related emissions during the day, and gridded inventories showed possible spatial mismatches. This approach can help cities better separate carbon dioxide sources and sinks.
Separating human-related carbon dioxide emissions from vegetation exchange in cities often...
Zhang et al. present a novel framework for partitioning urban eddy covariance CO₂ fluxes into anthropogenic and biogenic components in mixed-source environments where conventional isotopic or multi-species approaches are infeasible. The method is applied to Guangzhou and demonstrates good consistency with independent tracers (NOx, CO), wind-sector source area analyses, and traffic congestion patterns. Overall, the manuscript is well structured and clearly written, and it benefits from extensive sensitivity analyses. I only have a few minor comments that I hope will strengthen the paper.
The selection of the CH–PY site pair is reasonably justified on the grounds of shared regional climate and similar evergreen broadleaf forest composition. However, the manuscript would benefit from a more explicit discussion of the ecological comparability between the two sites. This would help readers assess whether the methodology is transferable to other cities. In particular, urban environments often host vegetation types that differ markedly from those in surrounding natural areas, and urban soils may exhibit distinct respiration characteristics due to irrigation, management practices, and other disturbances. Could these factors affect the general applicability of the approach to other urban settings?
More generally, I would encourage the authors to offer broader guidance on how eddy covariance systems, when equipped with this fossil-fuel separation technique, could be deployed to advance our understanding of urban carbon dynamics. At present, while all the results appear reasonable, the approach provides only limited insight into what is not known or what is uncertain about the city-wide fossil-fuel CO₂ emissions, except for the inventory-based proxies used here are not applicable at fine spatial scales. A more explicit discussion of the potential and limitations of this technique for informing urban emission monitoring and policy would greatly enhance the paper's impact.
In Section 3.4.3, the authors note that the PY footprint includes a substantial proportion of water bodies (~10%), which are commonly treated as zero-emission areas in emission inventories but may contribute to the total flux. How different assumptions regarding the treatment of water fractions might influence the inventory comparison?
In Section 3.2.1, the temperature-stratified GPP scheme is found to outperform both the pure saturating and VPD-inhibition structures, and is consequently adopted as the baseline. This is an interesting finding. However, the discussion could usefully acknowledge that temperature and VPD are often correlated, making it difficult to isolate their individual effects. It is plausible that the temperature-stratified scheme may indirectly capture VPD influences; this point merits brief consideration.
Figure 6a: it would be helpful to include monthly variations in Fcr as well, as what is done in Fig. 5.
Figure 7: given that both CO and CO₂ exhibit high background concentrations, the comparison based on percentage changes may be misleading.
I am unclear on the purpose of showing the illustrative 100% uncertainty range in Figure 10. Unless this serves a specific analytical or pedagogical role, I would suggest removing it or providing a clearer justification for its inclusion.