Quantification of anthropogenic and marine sources to atmospheric mercury over the marginal seas of China and impact assessment on the sea-air exchange of mercury

Qin, Xiaofei; Li, Hao; Chen, Jia; Wei, Junjie; Ding, Hao; Wang, Xiaohao; Wang, Guochen; Liu, Chengfeng; Lu, Da; Zhou, Shengqian; Li, Haowen; Zhu, Yucheng; Liu, Ziwei; Fu, Qingyan; Huo, Juntao; Lin, Yanfen; Deng, Congrui; Zhang, Yisheng; Huang, Kan

doi:https://doi.org/10.5194/egusphere-2025-623

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https://doi.org/10.5194/egusphere-2025-623

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21 Feb 2025

| 21 Feb 2025

Quantification of anthropogenic and marine sources to atmospheric mercury over the marginal seas of China and impact assessment on the sea-air exchange of mercury

Xiaofei Qin, Hao Li, Jia Chen, Junjie Wei, Hao Ding, Xiaohao Wang, Guochen Wang, Chengfeng Liu, Da Lu, Shengqian Zhou, Haowen Li, Yucheng Zhu, Ziwei Liu, Qingyan Fu, Juntao Huo, Yanfen Lin, Congrui Deng, Yisheng Zhang, and Kan Huang

Abstract. Mercury in the atmosphere is a crucial environmental concern due to its toxicity and ability to travel long distances. In the marginal seas, the contributions of terrestrial anthropogenic vs. natural sources on atmospheric mercury have been rarely quantified and their roles in mercury sea-air exchange are not well understood. To address this issue, this study integrated observations from island, cruise, and inland campaigns. Positive correlations were observed between total gaseous mercury (TGM) concentrations and environmental parameters such as temperature, relative humidity, and wind speed, indicating the significant influence of natural sources on atmospheric mercury in the marine environment. The application of the TGM/BC (black carbon) ratio underscored the effects of continental outflows. By utilizing a receptor model and linear regression analysis, a robust method was developed to quantitatively estimate the contribution of anthropogenic and natural sources to TGM. Anthropogenic sources accounted for an average of 59 %, 38 %, and 26 % of TGM over the Bohai Sea, Yellow Sea, and East China Sea, respectively. The sea-air exchange fluxes of mercury were estimated as 0.17±0.38, 1.10±1.34, and 3.44±3.24 ng m^-2 h^-1 over the three seas above, respectively. Upon omitting the contributions of anthropogenic sources, the sea-air exchange fluxes of mercury could be enhanced by 207.1 %, 32.4 %, and 5.8 %, respectively. This study elucidated the role of anthropogenic emissions in shaping the marine atmospheric mercury and the modulation of sea-air exchange fluxes, thereby informing valuable assessments regarding the influence of future reduced anthropogenic mercury emissions on the marine mercury cycle and ecosystems.

Received: 11 Feb 2025 – Discussion started: 21 Feb 2025

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Journal article(s) based on this preprint

27 Oct 2025

Quantification of anthropogenic and marine sources to atmospheric mercury over the marginal seas of China and impact on the sea–air exchange of mercury

Atmos. Chem. Phys., 25, 13815–13830, https://doi.org/10.5194/acp-25-13815-2025,https://doi.org/10.5194/acp-25-13815-2025, 2025

Short summary

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-623', Anonymous Referee #1, 13 Mar 2025
The authors conducted about one month (12/2/2020-1/1/2021), 3 weeks (10/14/2020-11/4/2020), and 2 weeks (12/29/2019-1/16/2020) of TGM measurements at two island sites, JHI and HNI, and on a cruise ship. They estimated anthropogenic contributions to ambient concentrations of TGM using PMF and linear regression analysis. They also estimated sea-air exchange flux of Hg° using an air-water exchange flux equation (Wanninkhof, 1992, JGR). Over the past couple of decades since the Tekran series has been deployed globally, numerous long-term datasets of speciated, operationally defined, mercury concentrations have been available and used to study atmospheric Hg cycling, which has generated a large body of research in the literature. While the authors performed a comprehensive analysis with what they got, the short-term nature of their datasets limited their ability to provide substantial insights into atmospheric Hg budgets. The study also presents several methodological concerns.

1. PMF application and interpretation issues. There are multiple concerns regarding the PMF application and interpretation:
The authors used PMF to identify the factors contributing to ambient TGM at DSL and JHI. DSL was a long-term (2015-2019) monitoring site over land near the coast in Shanghai, while the field campaign at HNI, an island site near DSL, took place a year later. The authors should clearly specify the time periods used in their PMF analysis for the two sites in Section 2.5.

The study assumes that the empirical relationship between anthropogenic TGM contributions and BC concentrations (derived from 2015–2019 land-based data) is applicable to TGM data at an island site over 100 km away and one year later. This assumption is highly questionable, as empirical relationships may not necessarily hold across different locations and timeframes. At the very least, the authors should acknowledge the potential uncertainty introduced by this approach.

The size of the dataset for JHI appears too small for PMF. It was a month-long campaign. What was the temporal resolution they had for their datasets? Fig. 5d seems to show about 20 data points. If this represented the number of data points for a single variable used for PMF analysis, then their results would be questionable. The authors should reference Zhang et al. (2009, https://doi.org/10.1016/j.atmosenv.2009.07.009), which discusses the minimum sample size required for PMF applications. That reference is just an example from a large body of literature on the topic. In fact, I am wondering, if the authors used the 2015-2019 data for DSL PMF analysis, how come only a handful of data points were shown in Fig. 5c?

The rationale behind selecting certain tracers is unclear. Why did the authors use V for the shipping emission tracer? The tracers for the cement industry were Ca and Fe, which could very well be indicative of dust. Why was BC not used in the PMF analysis?

2. TGM/BC ratio. The authors highlighted the TGM/BC ratio in the abstract as a key finding. However, this ratio appears to be just another variable rather than a novel result that provides additional insights.

3. Sea-Air exchange flux calculations. The study recalculates sea-air exchange flux after removing anthropogenic contributions from ambient data. However, the purpose of this recalculation remains unclear. This issue also relates to the statement in the abstract (Lines 39–40), which needs further clarification.

4. Unsupported assertions. Assertions throughout the manuscript lack supporting evidence or citations. Below are a few examples:
Line 256: The term “elucidation” is misleading, as the statement is purely speculative.

Lines 261–263, 265–266 (JHI), 266–268, 287: Assertions require supporting evidence or references.

Lines 281–300: This paragraph is speculative and lacks supporting evidence.

Line 319: The claim appears overstated.

Line 322: Requires a reference.

Lines 426–428: Wouldn’t higher temperatures enhance the partitioning of Hg° from water to air? The authors should clarify this mechanism.

Lines 427–428: Supporting evidence or references are needed for the statement.

Lines 430–431: Requires citations.

5. Insufficient methodological details. For the ancillary data of ion concentrations, trace gases, and meteorological variables, the authors provided little information on the instruments used, and no information on data quality control and assurance as well as temporal resolution. Also, where were the PBL data from? They were introduced abruptly at one point in the results section.

6. Random citations. Some references seemed to be cited arbitrarily. While citing every study on a given topic is impractical, it is important to acknowledge milestone research appropriately. Here are a few examples. There have been hundreds and thousands of journal articles on PMF applications. Did Qin et al. (2020) develop the PMF approach? Was Gibson et al. (2015) the first to recognize PMF “for its efficacy in elucidating sources profiles and quantifying source contributions”? In lines 280-281, were those studies the first to establish the role of temperature in GEM evasion? In lines 321-322, were those studies the first to identify fossil fuel combustion as a major mercury source?

7. Uncertainty in sea-air exchange flux calculations using TGM as a proxy for Hg° in sea-air exchange flux calculations could introduce significant uncertainty. While this may be reasonable in a landlocked atmosphere, it can be problematic in the marine boundary layer, where halogen compounds are abundant and subsequently GOM concentrations are probably not negligible at times. For example, Castagna et al. (2018, atmos. Env.) reported GOM reaching well over 100 pg/m³, ~10% of TGM, at times. Note that in the reference cited, GOM was measured using the Tekran series, which has been in the literature suggested to be largely under-biased, primarily by Gustin et al.’s team. The actual GOM concentrations may be even higher.
Citation: https://doi.org/10.5194/egusphere-2025-623-RC1
- AC1: 'Reply on RC1', Kan Huang, 27 May 2025
  
  Please find it in the attachement. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC1
RC2:
'Comment on egusphere-2025-623', Anonymous Referee #2, 16 Mar 2025

Please find in the attachment.

Citation: https://doi.org/10.5194/egusphere-2025-623-RC2
- AC3: 'Reply on RC2', Kan Huang, 27 May 2025
  
  Please find it in the attachment. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC3
RC3:
'Comment on egusphere-2025-623', Anonymous Referee #3, 17 Mar 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-623/egusphere-2025-623-RC3-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-623-RC3
- AC1: 'Reply on RC1', Kan Huang, 27 May 2025
  
  Please find it in the attachement. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC1
- AC2: 'Reply on RC3', Kan Huang, 27 May 2025
  
  Please find it in the attachment. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-623', Anonymous Referee #1, 13 Mar 2025
The authors conducted about one month (12/2/2020-1/1/2021), 3 weeks (10/14/2020-11/4/2020), and 2 weeks (12/29/2019-1/16/2020) of TGM measurements at two island sites, JHI and HNI, and on a cruise ship. They estimated anthropogenic contributions to ambient concentrations of TGM using PMF and linear regression analysis. They also estimated sea-air exchange flux of Hg° using an air-water exchange flux equation (Wanninkhof, 1992, JGR). Over the past couple of decades since the Tekran series has been deployed globally, numerous long-term datasets of speciated, operationally defined, mercury concentrations have been available and used to study atmospheric Hg cycling, which has generated a large body of research in the literature. While the authors performed a comprehensive analysis with what they got, the short-term nature of their datasets limited their ability to provide substantial insights into atmospheric Hg budgets. The study also presents several methodological concerns.

1. PMF application and interpretation issues. There are multiple concerns regarding the PMF application and interpretation:
The authors used PMF to identify the factors contributing to ambient TGM at DSL and JHI. DSL was a long-term (2015-2019) monitoring site over land near the coast in Shanghai, while the field campaign at HNI, an island site near DSL, took place a year later. The authors should clearly specify the time periods used in their PMF analysis for the two sites in Section 2.5.

The study assumes that the empirical relationship between anthropogenic TGM contributions and BC concentrations (derived from 2015–2019 land-based data) is applicable to TGM data at an island site over 100 km away and one year later. This assumption is highly questionable, as empirical relationships may not necessarily hold across different locations and timeframes. At the very least, the authors should acknowledge the potential uncertainty introduced by this approach.

The size of the dataset for JHI appears too small for PMF. It was a month-long campaign. What was the temporal resolution they had for their datasets? Fig. 5d seems to show about 20 data points. If this represented the number of data points for a single variable used for PMF analysis, then their results would be questionable. The authors should reference Zhang et al. (2009, https://doi.org/10.1016/j.atmosenv.2009.07.009), which discusses the minimum sample size required for PMF applications. That reference is just an example from a large body of literature on the topic. In fact, I am wondering, if the authors used the 2015-2019 data for DSL PMF analysis, how come only a handful of data points were shown in Fig. 5c?

The rationale behind selecting certain tracers is unclear. Why did the authors use V for the shipping emission tracer? The tracers for the cement industry were Ca and Fe, which could very well be indicative of dust. Why was BC not used in the PMF analysis?

2. TGM/BC ratio. The authors highlighted the TGM/BC ratio in the abstract as a key finding. However, this ratio appears to be just another variable rather than a novel result that provides additional insights.

3. Sea-Air exchange flux calculations. The study recalculates sea-air exchange flux after removing anthropogenic contributions from ambient data. However, the purpose of this recalculation remains unclear. This issue also relates to the statement in the abstract (Lines 39–40), which needs further clarification.

4. Unsupported assertions. Assertions throughout the manuscript lack supporting evidence or citations. Below are a few examples:
Line 256: The term “elucidation” is misleading, as the statement is purely speculative.

Lines 261–263, 265–266 (JHI), 266–268, 287: Assertions require supporting evidence or references.

Lines 281–300: This paragraph is speculative and lacks supporting evidence.

Line 319: The claim appears overstated.

Line 322: Requires a reference.

Lines 426–428: Wouldn’t higher temperatures enhance the partitioning of Hg° from water to air? The authors should clarify this mechanism.

Lines 427–428: Supporting evidence or references are needed for the statement.

Lines 430–431: Requires citations.

5. Insufficient methodological details. For the ancillary data of ion concentrations, trace gases, and meteorological variables, the authors provided little information on the instruments used, and no information on data quality control and assurance as well as temporal resolution. Also, where were the PBL data from? They were introduced abruptly at one point in the results section.

6. Random citations. Some references seemed to be cited arbitrarily. While citing every study on a given topic is impractical, it is important to acknowledge milestone research appropriately. Here are a few examples. There have been hundreds and thousands of journal articles on PMF applications. Did Qin et al. (2020) develop the PMF approach? Was Gibson et al. (2015) the first to recognize PMF “for its efficacy in elucidating sources profiles and quantifying source contributions”? In lines 280-281, were those studies the first to establish the role of temperature in GEM evasion? In lines 321-322, were those studies the first to identify fossil fuel combustion as a major mercury source?

7. Uncertainty in sea-air exchange flux calculations using TGM as a proxy for Hg° in sea-air exchange flux calculations could introduce significant uncertainty. While this may be reasonable in a landlocked atmosphere, it can be problematic in the marine boundary layer, where halogen compounds are abundant and subsequently GOM concentrations are probably not negligible at times. For example, Castagna et al. (2018, atmos. Env.) reported GOM reaching well over 100 pg/m³, ~10% of TGM, at times. Note that in the reference cited, GOM was measured using the Tekran series, which has been in the literature suggested to be largely under-biased, primarily by Gustin et al.’s team. The actual GOM concentrations may be even higher.
Citation: https://doi.org/10.5194/egusphere-2025-623-RC1
- AC1: 'Reply on RC1', Kan Huang, 27 May 2025
  
  Please find it in the attachement. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC1
RC2:
'Comment on egusphere-2025-623', Anonymous Referee #2, 16 Mar 2025

Please find in the attachment.

Citation: https://doi.org/10.5194/egusphere-2025-623-RC2
- AC3: 'Reply on RC2', Kan Huang, 27 May 2025
  
  Please find it in the attachment. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC3
RC3:
'Comment on egusphere-2025-623', Anonymous Referee #3, 17 Mar 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-623/egusphere-2025-623-RC3-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-623-RC3
- AC1: 'Reply on RC1', Kan Huang, 27 May 2025
  
  Please find it in the attachement. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC1
- AC2: 'Reply on RC3', Kan Huang, 27 May 2025
  
  Please find it in the attachment. Thanks!
  
  Citation: https://doi.org/10.5194/egusphere-2025-623-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Kan Huang on behalf of the Authors (27 May 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (28 May 2025) by Aurélien Dommergue

RR by Anonymous Referee #1 (11 Jun 2025)

Suggestions for revision or reasons for rejection

The authors have addressed my questions and comments about their PMF application and a few specific comments.

However, I have a few remaining concerns. First, I don’t find their response convincing, especially to one of the main questions I had: why did they estimate the oceanic evasion flux of GEM after removing the anthropogenic contribution? This estimation is after all the crux of the study. They replied that “(t)he purpose of this recalculation is to assess potential changes in the marine mercury flux under the scenario of reduced anthropogenic emissions”. But does this imply that anthropogenic emissions should not be reduced simply because oceanic evasion might increase as a result? According to Eq. 1, removing all the anthropogenic contribution in Ca, while assuming that Cw remains unchanged, increases the concentration gradient and thereby inflates the estimated evasion flux. The issue is whether Cw would in fact remain constant if all anthropogenic contributions were eliminated. I suspect not—particularly in near-coastal regions, where reduced Ca would likely lead to less dry deposition to the ocean surface, and thus a decrease in Cw. Ignoring this positive feedback between anthropogenic emissions and oceanic evasion is, in my view, a major flaw in the study.

Second, the authors simply removed the TGM/BC ratio originally presented in the abstract in response to my question regarding its significance. However, the relevant content remains in the manuscript without further consideration. Even the original values were not updated despite the use of a different dataset and recalculated values (See Lines 428-436). BC concentrations naturally decreased over the ocean as the distance from the coast increased. However, TGM concentrations are more complex, particularly given the authors' own finding of a significant oceanic source for GEM. The pattern of TGM/BC varied between region south and north of ~32.5 ⁰N: south of this latitude, the ratio increases with increasing distances from the coast, whereas the pattern to the north is more complicated. This suggests that the TGM/BC ratio is rather confounding, doesn’t it? I have no objection to including this result in the manuscript, but the authors must provide a more meaningful discussion of its implications, which is currently lacking.

Third, I still do not fully understand the authors’ choice of the time period for the DSL data. In the original manuscript, I pointed out a mismatch in time periods: DSL data from 2015-2019 and HNI from 10/14/2020-11/4/2020. This was a critical concern, as the authors used the anthropogenic portion of ambient TGM, regressed as a function of BC concentrations from DSL, to further estimate the oceanic flux at HNI. In the revised version, they used DSL data from Oct-Dec 2020, still not a match with the HNI period. Wouldn’t the author want to justify their selection of the DSL time frame? Additionally, to separate anthropogenic TGM from TGM in the cruise measurements over the ECS and YS, the authors used the anthropogenic TGM-BC relationship derived from DSL data when backward trajectories originated from DSL, and used a relationship from JHI when backward trajectories were northerly. However, the JHI period also mismatched the cruise measurement periods. In doing so, the authors seemed to assume that the anthropogenic GEM-BC relationship remains constant over time. If this is indeed their underlying assumption, shouldn’t they make an effort to demonstrate its validity?

Fourth, one of my specific comments on the original version was about their explanation for the effect of temperature on seasonal variation in DGM. What they added in lines 557 – 560 merely stated the opposing effects of temperature and didn’t really explain how such opposing effects resulted in the seasonal variation their data showed.

A couple of specific comments:

Lines 374 – 376: I don’t recall Lin et al. (2010b) and Soerensen et al. (2013) suggested that “wetting processes may promote the reduction of Hg to Hg0 in surface seawater”. Through what mechanism?

Line 499: How was that 5% uncertainty estimated?

Lines 588 – 590: I disagree that what they did underscored “the potential effects of diminished anthropogenic emissions on oceanic mercury cycling”. See my first comment above.

Hide

ED: Reconsider after major revisions (11 Jun 2025) by Aurélien Dommergue

AR by Kan Huang on behalf of the Authors (06 Aug 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (22 Aug 2025) by Aurélien Dommergue

AR by Kan Huang on behalf of the Authors (23 Aug 2025)

Journal article(s) based on this preprint

27 Oct 2025

Quantification of anthropogenic and marine sources to atmospheric mercury over the marginal seas of China and impact on the sea–air exchange of mercury

Atmos. Chem. Phys., 25, 13815–13830, https://doi.org/10.5194/acp-25-13815-2025,https://doi.org/10.5194/acp-25-13815-2025, 2025

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Mercury is a persistent toxic pollutant that has equally important anthropogenic and natural sources. This study developed a quantitative method on separating the anthropogenic and natural contributions of total gaseous mercury. The underlying impacts on the sea-air exchange fluxes of mercury are evaluated. The new method developed in this study can be reproducible in other regions and the findings are innovative in the field of mercury sources and biogeochemical cycles.


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