the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jul 2025 | Subsequently updated
NOx emissions changes from 2019 to 2021 in Eastern China as estimated through variational inversions and TROPOMI satellite data
Abstract. China is one of the largest emitters of nitrogen oxides NOx (= NO + NO2) worldwide, and up-to-date estimates are crucial as the country faces rising pressure to curb emissions. We estimate NOx emissions over Eastern China (101.75–132.25° E; 17.75–50.25° N) from 2019 to 2021, focusing on the impacts of COVID-19 and the Chinese Lunar New Year (LNY). Using high-resolution NO2 observations from TROPOMI, onboard the Sentinel-5 Precursor satellite, our estimates are at the regional, national and provincial scales. They are produced using the Community Inversion Framework (CIF), coupled to the CHIMERE regional chemistry transport model at 0.5° resolution.
Our results show a sharp drop in NOx emissions by −40% in February 2020, as compared to 2019, driven mostly by lockdown-related mobility restrictions, and partially due to LNY festivities. Provincial reductions in February 2020 include −38% in Shanghai, −29% in Qinghai, −31% in Jiangsu, −36% in Hubei, −24% in Henan, and −16% in Beijing. Total NOx emissions (anthropogenic + biogenic) over Eastern China fell by 0.2 TgNO2/year in 2020 vs. 2019, but rose again in 2021, exceeding 2019 levels by +4% (16.7 TgNO2 in 2021 vs 16.0 TgNO2 in 2019).
Our estimates of recent past years offer insights to guide future strategies and policies to reduce NOx emissions in China and its provinces. These results highlight the advantages of combining high dimensional variational inversion methods with high-resolution satellite data, to strengthen air quality monitoring and support more effective regulations.
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Version 2 | 24 Oct 2025
RC1: 'Comment on egusphere-2025-3329', Anonymous Referee #1, 11 Dec 2025 reply -
Version 1 | 17 Jul 2025
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- 1
Audrey Fortems-Cheiney
Grégoire Broquet
Robin Plauchu
Isabelle Pison
Antoine Berchet
Elise Potier
Gaëlle Dufour
Adriana Coman
Dilek Savas
Guillaume Siour
Henk Eskes
Beatriz Revilla-Romero
Antony Delavois
Philippe Ciais
- V1 , 17 Jul 2025
This study quantifies changes in NOx emissions over eastern China from 2019 to 2021, with particular attention to the reduction associated with the COVID-19 lockdowns and the Chinese Lunar New Year. A noteworthy aspect is that the authors derive total NOx emissions, encompassing both anthropogenic and biogenic contributions. Given the substantial reduction from 2019 to 2020, which is likely driven by government interventions associated with COVID-19 restrictions and the Lunar New Year slowdown, and considering the clear rebound in 2021, the interannual pattern reported in the study appears reasonable. The manuscript provides useful insights and is, overall, clearly presented and well-structured. However, certain critical details require clarification. I support the publication of this manuscript, provided that the following major comments can be addressed.
Major comments:
In this study (line 214), the NOx emission estimates are reported in teragrams of nitrogen dioxide equivalent (Tg NO2) for both the prior and posterior datasets. I am not fully familiar with this convention and would appreciate clarification on the rationale for adopting NO2-equivalent units, particularly in comparison with more conventional representations such as NOx expressed directly in tons. Given that this study relies on TROPOMI observations with high spatial resolution and daily temporal coverage, rather than annual or monthly averaged emission inventories, it is not immediately clear whether expressing emissions in Tg NO2 offers advantages or disadvantages when dealing with such pronounced temporal and spatial heterogeneity.
This study combines several emission inventories to represent different sectors and applies multiple downscaling approaches to convert monthly emission estimates into daily values. A key issue arises in Figure 2 (line 217) and Figure S1 to S3, where prior emission values below 0.3 kt NO2 in February 2019 are set to zero when computing the relative difference in order to reduce noise in the figure. It is well understood that extremely small values in prior fields often reflect numerical noise rather than actual emissions. However, the manuscript only discusses this adjustment in the context of figure presentation and does not clearly explain how grids with prior values below 0.3 kt NO2 are treated during the inversion itself. Specifically, it remains unclear whether these low-value regions are assimilated as noisy priors, masked out, or handled with any constraint in the retrieval process.
Figure 9 is discussed only briefly, yet it shows a monthly trend that differs substantially from the posterior estimates. A deeper analysis is recommended to clarify the potential drivers of this discrepancy and to explain the underlying causes of the observed emission pattern.
Although the study claims advantages in high temporal resolution, the daily emission variations are not clearly demonstrated. Most results remain at monthly or seasonal scales, and even in Figure 11, the time series which is about two months for Henan is shown with a 14-day moving average, which overly smooths the data. This presentation does not effectively showcase the value of daily-scale emission information.
As shown in Figures 13 and 14, the year 2020 marks not only the onset of the COVID-19 lockdowns but also the final year of China’s 13th Five-Year Plan. Although many studies have reported substantial emission reductions during this period, attributing these changes solely to the lockdown measures is not appropriate, especially given the large spatial and socio-economic heterogeneity of the cities examined. Broader policy-driven structural adjustments and long-term emission control measures likely contributed as well, and should therefore be considered in the interpretation. To avoid potential over-attribution, the authors are encouraged to either (1) clarify how the analysis distinguishes the lockdown-induced reductions from broader economic influences, or (2) revise the discussion to reflect that the observed changes may result from both the lockdown measures and underlying economic conditions.
Minor comments:
The title of Section 3.3, “Seasonal cycle of NOx total emissions in Eastern China” appears to reflect only the content associated with Figure 9. However, Figures 10 and 11 focus on the NOx emission changes around the Lunar New Year period, which does not fall under the concept of a seasonal cycle. To avoid confusion and more accurately represent the scope of this section, it would be clearer to revise the title of Section 3.3 to better encompass both the seasonal analysis and the LNY-related emission variations.
Line 477 contains only a single sentence presented as an independent paragraph. It would be clearer and more consistent with academic writing conventions either to merge this sentence with the preceding or following paragraph.
In Figure 10 and 11, the comparison of different weeks following the Lunar New Year provides useful insight into the holiday-related impact on NOx emissions in China. However, when comparing week-to-week trends across different years, is the underlying daily grid spatial coverage consistent among the three years. If the number of valid emission grids in 2020 differs from (maybe much smaller than) those in 2019 and 2021, the conclusion regarding the pandemic-related disruption would not be fully robust.
In Figure 10, connecting the data points for different provinces with lines adds little interpretive value. The key information lies in whether the relative changes are above or below zero, similar to the Figure 13. It is recommended to remove these lines and consider alternative ways to visually highlight the direction and magnitude of the changes, which would improve the clarity and readability of the figure.
The title of Section 3.4, “Impact of the COVID-19 lockdowns in Eastern China”, suggests that the analysis only focus on the effects of the lockdowns. However, as indicated in lines 525 to 530, part of the observed emission reductions may also stem from concurrent economic downturns or other many factors which are not related with lockdown. It is not sufficiently precise to use the current title and framing of Section 3.4 when other factors, such as economic slowdown, also contribute to the observed emission reductions.