Acta Optica Sinica, Volume. 45, Issue 12, 1201005(2025)

Construction of High-Resolution Grid for Urban Anthropogenic and Biogenic CO2 Fluxes Using Multi-Source Data

Erchang Sun1...2,3, Xianhua Wang1,2,3,*, Hanhan Ye1,3, Shichao Wu1,3, Hailiang Shi1,2,3, Chao Li1,2,3, and Yuan An1,23 |Show fewer author(s)
Author Affiliations
  • 1Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui , China
  • 2University of Science and Technology of China, Hefei 230026, Anhui , China
  • 3Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230031, Anhui , China
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    Objective

    Urban areas contribute approximately 70% of global anthropogenic carbon dioxide (CO2) emissions, making them a key area in carbon monitoring efforts. The “top-down” approach, which uses measured atmospheric CO2 concentrations, allows for near real-time emission estimates on a global urban scale and serves as a crucial tool for verifying urban emission reductions. Currently, the prior estimates of urban CO2 fluxes in top-down assessments rely on data from open-source data inventory for anthropogenic CO2 (ODIAC) and vegetation photosynthesis and respiration model (VPRM). However, these prior fluxes possess high spatial uncertainty, resulting in significant bias in urban emission estimates and failing to meet the sub-kilometer resolution required for urban grids. In our study, we construct a high-resolution spatial and temporal dataset for urban CO2 fluxes by integrating multi-source data. We also evaluate the effect of this spatial optimization using column-averaged dry-air mole fraction of CO2 (XCO2) data from the orbiting carbon observatory-3 (OCO-3) satellite. The results indicate that using the optimized CO2 fluxes enables more accurate simulations of local CO2 concentration variations, achieving a closer match with observations. Our high-resolution urban CO2 flux dataset can contribute to reducing uncertainty in CO2 flux estimates and provide more accurate prior values for “top-down” urban emission estimates.

    Methods

    For CO2 fluxes, there are significant spatial dependencies. Anthropogenic emissions mainly come from fixed sources such as power plants, transportation networks, and industrial zones, while biogenic fluxes are concentrated in vegetation-covered areas like forests, croplands, and grasslands. To represent these spatial patterns, we use land cover types as proxies for CO2 fluxes. For anthropogenic CO2 emissions, we utilize datasets such as the global power plant database, OpenStreetMap, and the essential urban land use categories (EULUC), which offer detailed representations of emissions from power plants, industry, residential areas, and transportation networks. For biogenic CO2 fluxes, we select the WorldCover land cover dataset to distinguish key land cover types, including forests, croplands, and grasslands. The construction of CO2 flux grids involves specific methodologies for anthropogenic and biogenic fluxes. For anthropogenic emissions, we utilize sector-specific, grid-based emission data from the multi-resolution emission inventory for China (MEIC) and process spatial proxy data grid by grid to accurately allocate total emissions across geographic regions. For biogenic fluxes, we estimate flux factors for various vegetation types and integrate them with land use data to calculate precise flux values for each vegetation category. To validate the CO2 flux datasets, we adopt an indirect evaluation approach. We assess the accuracy of the constructed datasets by comparing observed and simulated CO2 concentrations. Simulations are carried out using the stochastic time-inverted Lagrangian transport (STILT) model, and the outputs are validated against XCO2 observations from the OCO-3 satellite. This approach provides a robust evaluation of the spatial representation of CO2 fluxes and their alignment with observed atmospheric CO2 distributions.

    Results and Discussions

    In our study, we take Hefei as a case study to develop a high-resolution urban CO2 flux grid with a spatial resolution of 0.002°×0.002° (Figs. 3 and 4). The constructed grid data effectively captures the detailed distribution characteristics of CO2 sources and sinks, which are not well represented in previous datasets. We compare the spatial patterns of the improved CO2 emissions with those from the MEIC and ODIAC datasets (Figs. 5 and 6). Additionally, we analyze the changes in biogenic CO2 fluxes before and after optimization using remote sensing imagery with a spatial resolution finer than 1 m (Fig. 8). To evaluate the effectiveness of the CO2 flux optimization, we employ the X-STILT model to simulate XCO2 concentrations based on both pre- and post-optimization CO2 flux data. These simulations are then validated against XCO2 observations from the OCO-3 satellite. The validation utilizes OCO-3 data from three observations: June 16, 2022 (Fig. 9), June 4, 2021 (Fig. 11), and October 11, 2020 (Fig. 12).

    Conclusions

    In the present study, we develop a high-resolution grid of urban anthropogenic and biogenic CO2 fluxes by integrating effective information from multi-source datasets with varying formats, spatial resolutions, and temporal coverage. We validate and evaluate the spatial optimization of CO2 fluxes using observational data from OCO-3. The analysis highlights pronounced local spatial heterogeneity in urban anthropogenic CO2 emissions. Strong point sources, such as power plants, and weaker sources, such as residential areas, lead to significantly different variations in local CO2 concentrations. Coarse-resolution emission data tend to average these differences in simulations, making it difficult to capture localized CO2 peaks. Compared to ODIAC data, the spatially optimized emission data substantially refine the urban CO2 emission distribution, transforming it from a “Gaussian-like” pattern to a “multi-centered” distribution. For biogenic CO2 fluxes, the optimized data successfully identify small-scale urban green spaces, enabling a more precise simulation of vegetation’s influence on local CO2 concentration dynamics. Using the WRF-XSTILT model, we compare simulations of XCO2 concentrations before and after optimization against OCO-3 observations. The results show significant improvements in both validation cases: correlation coefficients increase from 0.26 to 0.46, from 0.62 to 0.73, and from 0.50 to 0.60, respectively, while biases decrease from 1.36×10-6 to 1.24×10-6, from 0.87×10-6 to 0.80×10-6 and from 0.80×10-6 to 0.73×10-6. These findings underscore the enhanced capability of the optimized data to accurately represent the spatial distribution of CO2 fluxes.

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    Erchang Sun, Xianhua Wang, Hanhan Ye, Shichao Wu, Hailiang Shi, Chao Li, Yuan An. Construction of High-Resolution Grid for Urban Anthropogenic and Biogenic CO2 Fluxes Using Multi-Source Data[J]. Acta Optica Sinica, 2025, 45(12): 1201005

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Nov. 11, 2024

    Accepted: Dec. 25, 2024

    Published Online: Apr. 18, 2025

    The Author Email: Wang Xianhua (xhwang@aiofm.ac.cn)

    DOI:10.3788/AOS241741

    CSTR:32393.14.AOS241741

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