the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Sep 2025
Atmospheric CO2 dynamics in a coastal megacity: spatiotemporal patterns, sea-land breeze impacts, and anthropogenic-biogenic emission partitioning
Abstract. Accurate quantification of urban carbon dioxide (CO2) emissions is essential for evaluating the efficacy of urban climate mitigation policies. However, the complex interplay of anthropogenic emissions, biogenic fluxes, and meteorological processes in coastal megacities poses significant challenges to characterizing urban CO2 dynamics. To address this, we present an observation-based framework that integrates high-precision CO2 monitoring, meteorological analyses, and ΔCO/ΔCO2 ratios (Rco) to resolve spatiotemporal CO2 variations, quantify sea-land breeze (SLB) effects, and partition anthropogenic and biogenic contributions. Applied in Guangzhou, a coastal megacity, our approach captures a pronounced urban–rural gradient. The coastal site shows the largest seasonal amplitude (25.63 ppm), resulting from wintertime transport of urban emissions and summertime inflow of marine air. Diurnally, suburban CO2 variations are dominated by biogenic activity (summer amplitude: 39.90 ppm), while urban signals reflect anthropogenic influence. SLB generally reduces coastal CO2 by 5.87 ppm but leads to a summer accumulation (+2.08 ppm) under stable, low-wind conditions with shallow boundary layers. Regression-derived Rco values (urban: 7.45 ± 1.38 ppb ppm⁻1) reflect improved combustion efficiency linked to clean-air policies. Importantly, our combined observational, modeling, and Rco framework reveals that biogenic fluxes offset 60.17 % of anthropogenic CO2 emissions during summer afternoons. The framework is validated against emission inventories, Normalized Difference Vegetation Index data, and independent studies, demonstrating its robustness. This study enhances process-oriented understanding of coastal carbon cycling and underscores the integration of meteorological and biospheric dynamics in urban CO2 assessments.
- Preprint
(2294 KB) - Metadata XML
-
Supplement
(2112 KB) - BibTeX
- EndNote
Status: open (until 15 Nov 2025)
- RC1: 'Comment on egusphere-2025-3215', Anonymous Referee #2, 21 Oct 2025 reply
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,751 | 53 | 12 | 1,816 | 40 | 18 | 15 |
- HTML: 1,751
- PDF: 53
- XML: 12
- Total: 1,816
- Supplement: 40
- BibTeX: 18
- EndNote: 15
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This paper focuses on the atmospheric CO₂ dynamics in Guangzhou, a coastal megacity. It does this by developing an observation-based framework that integrates ground-based CO and CO₂ observations, as well as ΔCO/ΔCO₂ ratios. The paper analyses the spatiotemporal patterns of CO₂, assesses the influence of sea–land breezes and partitions anthropogenic and biogenic emissions. While the study presents interesting findings regarding Guangzhou’s carbon emissions, some clarification of the results is necessary. The manuscript could be improved by providing more comprehensive interpretations of the methods and results, particularly with regard to the uncertainty of the results presented. Please see my comments below.
General comments:
(1) CO was measured simultaneously, but is only analysed in Section 3.4. Since CO is a good proxy for CO₂ over urban regions, it would be interesting to check the CO results, for example in Figures 4 and 6. Please show and interpret the diurnal cycle of CO in contrast to CO₂.
(2) Please explain how the error bars in Figure 9 are calculated. It seems to me that the uncertainty is so large that the difference between summer and winter is not significant. Also, what is the uncertainty of the estimation in line 434 (60.17%)?
(3) The manuscript identifies sea–land breeze (SLB) days based on 24-hour wind direction transitions and a wind speed threshold of <10 m s⁻¹. While this threshold excludes most strong winds, the authors should clarify whether the potential influence of tropical cyclones or their peripheral circulations was considered. Even when wind speeds remain below 10 m s⁻¹, such events can disrupt local wind directions, potentially disturbing the regular daytime–night-time SLB pattern. Without addressing these effects, SLB identification and subsequent CO₂ dynamics interpretation may be biased.
Other comments:
Although the introduction highlights three key knowledge gaps in the existing research, it does not emphasise their relevance to coastal cities enough, nor does it distinguish these gaps from those in studies of inland cities. Further descriptions are required. While the introduction mentions Guangzhou’s GDP, population, green coverage and sea–land breeze frequency, it does not link these to the study objectives. Green coverage, which is important for biogenic fluxes, is neither compared with that in other coastal cities nor discussed in terms of its impact on flux magnitude. The frequency of the sea–land breeze is cited without detailing its seasonal patterns or how it differs from that in other cities. Furthermore, Guangzhou's carbon mitigation policies, which could influence anthropogenic and biogenic CO₂ emissions, are not mentioned.
Line 25: clean air policies? Please provide a little bit more details of these policies in the abstract.
Line 114: EDGAR full name.
Figure 8: Please show the density plot; the points may overlap strongly with each other.
Line 448: is the EDGAR mean yearly or monthly? How is the temporal resolution of EDGAR done? How are daytime and night-time emissions differentiated?
The STILT model releases 500 particles with a 72-hour backward trajectory and a spatial resolution of 0.08° × 0.08°, but no sensitivity tests are reported. It is unclear whether increasing the number of particles or improving the spatial resolution would significantly affect the footprint simulations. These model parameters directly impact the accuracy of CO₂ emission estimates, so relevant validation analyses are essential.
Figure 2: The distinction between the different wind directions is unclear. The authors should consider optimising the figure, for example by using more distinct colours, line styles or annotations, to improve clarity and readability.
Figure 7: It is recommended that the legend be placed outside the figure.
Line 195: ΔCO₂ appears improperly formatted.
On line 288, 'Despite CH's stronger biogenic coupling (NDVI correlation: −0.72; Fig. 3f), NS's CO₂ levels remained 9.80 ppm lower than CH in summer and 5.80 ppm higher in winter, underscoring transport-dominated over biogenic controls at the coastal site.' This whole sentence should be rephrased to enhance logical rigour.
On line 324, it states that, at CH, 'smaller daytime weekday–weekend differences suggest that biogenic fluxes outweigh anthropogenic variations'. This statement is somewhat too absolute. It would be better to acknowledge the uncertainties more appropriately and consider the potential influence of atmospheric transport and boundary layer dynamics.