the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Aug 2022
Enhanced Natural Releases of Mercury in Response to Reduction of Anthropogenic Emissions during the COVID-19 Lockdown by Explainable Machine Learning
Abstract. The widespread of coronavirus (COVID-19) has significantly impacted the global human activities. Compared to numerous studies on conventional air pollutants, atmospheric mercury that has matched sources from both anthropogenic and natural emissions is rarely investigated. At a regional site in Eastern China, an intensive measurement was performed, showing obvious decreases of gaseous elemental mercury (GEM) during the COVID-19 lockdown, while not as significant as the other air pollutants. Before the lockdown when anthropogenic emissions dominated, GEM showed no correlation with temperature and negative correlations with wind speed and the height of boundary layer. In contrast, GEM showed significant correlation with temperature while the relationship between GEM and wind speed/boundary layer disappeared during the lockdown, suggesting the enhanced natural emissions of mercury. By applying a machine learning model and the Shapley Additive ExPlanation Approach, it was found that the mercury pollution episodes before the lockdown were driven by anthropogenic sources, while they were mainly driven by natural sources during and after the lockdown. Source apportionment results showed that the absolute contribution of natural surface emissions to GEM unexpectedly increased (44%) during the lockdown. Throughout the whole study period, a significant negative correlation was observed between the absolute contribution of natural and anthropogenic sources to GEM. We conclude that natural release of mercury could be stimulated to compensate the significantly reduced anthropogenic GEM via the surface - air exchange balance of mercury.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1092 KB) - Metadata XML
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Supplement
(531 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
- RC1: 'Comment on egusphere-2022-773', Anonymous Referee #1, 24 Sep 2022
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RC2: 'Comment on egusphere-2022-773', Paula Harder, 05 Oct 2022
This work investigates how anthropogenic and natural mercury emissions differ before, during, and after the COVID-19 lockdown. The paper aims to show that the decrease in anthropogenic mercury emissions during the lockdown led to an increase in natural release. The methods used to quantify that are correlation analysis, a PMF model and a neural network (NN) combined with SHAP values. The NN learned to predict gaseous elemental mercury (GEM) given other air pollutants and atmospheric conditions and then applies the SHAP approach to obtain how much each input feature contributed to the prediction. The choice of method, using an NN with SHAP values, seems suitable for this setting. The performance of the NN is not very good, this will also affect the interpretability of the SHAP values. In general, more details of the NN would be helpful.Â
From my point of view, this paper needs rewriting and changes before it can be accepted for publication at ACP.
Further comments:
Line 106: The PMF approach should be mentioned in the introduction
Line 155: Please include a few details of the NN in this work, such as data size, how the train-val-test split was done, and a comment on hyperparameter tuning
Line 284: R² values are missing in three out of the nine subplots
Line 284: What are the lines shown in the plots and why aren’t they shown in each of the subplots
Line 293: The R² score of 0.67 is okay, but not great.Â
Line 293: It would be relevant to know the different performances of the NN evaluated on the pre-lockdown, lockdown, and post-lockdown periods.
Citation: https://doi.org/10.5194/egusphere-2022-773-RC2 - AC1: 'Response to Reviewers' Comments (egusphere-2022-773)', Kan Huang, 10 Nov 2022
Interactive discussion
Status: closed
- RC1: 'Comment on egusphere-2022-773', Anonymous Referee #1, 24 Sep 2022
-
RC2: 'Comment on egusphere-2022-773', Paula Harder, 05 Oct 2022
This work investigates how anthropogenic and natural mercury emissions differ before, during, and after the COVID-19 lockdown. The paper aims to show that the decrease in anthropogenic mercury emissions during the lockdown led to an increase in natural release. The methods used to quantify that are correlation analysis, a PMF model and a neural network (NN) combined with SHAP values. The NN learned to predict gaseous elemental mercury (GEM) given other air pollutants and atmospheric conditions and then applies the SHAP approach to obtain how much each input feature contributed to the prediction. The choice of method, using an NN with SHAP values, seems suitable for this setting. The performance of the NN is not very good, this will also affect the interpretability of the SHAP values. In general, more details of the NN would be helpful.Â
From my point of view, this paper needs rewriting and changes before it can be accepted for publication at ACP.
Further comments:
Line 106: The PMF approach should be mentioned in the introduction
Line 155: Please include a few details of the NN in this work, such as data size, how the train-val-test split was done, and a comment on hyperparameter tuning
Line 284: R² values are missing in three out of the nine subplots
Line 284: What are the lines shown in the plots and why aren’t they shown in each of the subplots
Line 293: The R² score of 0.67 is okay, but not great.Â
Line 293: It would be relevant to know the different performances of the NN evaluated on the pre-lockdown, lockdown, and post-lockdown periods.
Citation: https://doi.org/10.5194/egusphere-2022-773-RC2 - AC1: 'Response to Reviewers' Comments (egusphere-2022-773)', Kan Huang, 10 Nov 2022
Peer review completion
Journal article(s) based on this preprint
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Xiaofei Qin
Shengqian Zhou
Hao Li
Guochen Wang
Cheng Chen
Chengfeng Liu
Xiaohao Wang
Juntao Huo
Yanfen Lin
Jia Chen
Qingyan Fu
Yusen Duan
Kan Huang
Congrui Deng
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1092 KB) - Metadata XML
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Supplement
(531 KB) - BibTeX
- EndNote
- Final revised paper