the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Integrating Point Sources to Map Anthropogenic Atmospheric Mercury Emissions in China, 1978–2021
Abstract. Mercury emissions from human activities persist in the environment, posing risks to humans and ecosystem, and are regulated by the Minamata Convention. Understanding the historical emissions of mercury is critical for explaining the presence of mercury in the environment. In recent years, some studies have looked at the historical trends of atmospheric emission inventory. The spatial resolution of inventories for relatively recent years have improved. However, limited inventories have combined both long time scales and high spatial resolution, which is essential for evaluating the legacy impacts of anthropogenic mercury emissions, particularly in regions with high levels of mercury emissions. Here we compile a new comprehensive point source database by fusing multiple data source, and integrate it with previous China Atmospheric Mercury Emission Model to create an annual point source and gridded emission inventory for China covering 1978–2021. Integrating point source emission inventory (P-CAME) improves the accuracy of the gridded emissions, reducing the normalized mean error for all grids by 108 % compared to not using point sources in the most recent year of 2021. The improved gridded emissions inventory notably enhances the simulation of atmospheric mercury concentrations, particularly in urban areas. P-CAME inventory resulted in a 20–23 % reduction in the normalized mean bias. The improved gridded emission data identifies potential polluted grids characterized by high cumulative emissions. It indicates that 20 % of cumulative emissions originate from just 0.3 % of the grids, primarily distributed in Gansu, Yunnan, and Hunan Provinces. These areas are predominantly dominated by non-ferrous metal smelters or a mix of emissions sources including coal-fired industries and cement production. With the improvements in simulation accuracy and the identification of highly polluted regions, this updated inventory would greatly facilitate the assessment of mercury exposure, legacy impacts, and effective management of cross-media mercury pollution.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2024-252', Anonymous Referee #1, 28 Sep 2024
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RC2: 'Comment on essd-2024-252', Anonymous Referee #2, 02 Oct 2024
General Comments:
This manuscript presents a novel approach to improving the annual mercury emissions inventory for China from 1978 to 2021 using the P-CAME model. The work is important for understanding mercury emissions in the region and for supporting policy measures under the Minamata Convention. However, the validation section requires significant improvement to ensure the reliability of the data and the robustness of the conclusions. Below are some suggestions to enhance the manuscript before it can be considered for publication:
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Validation over the Entire Study Period: The study covers a long time span (1978–2021), with peak emissions identified around 2010–2012, as shown in Figure 3. However, the model evaluation is limited to the year 2021, which represents a period of reduced emissions compared to the peak years. This raises concerns about whether the model performs well in earlier years, especially around the time of peak emissions. To address this issue, the authors should include validation for multiple years, particularly around periods of significant changes in emissions, such as 2010–2012. If observational data from earlier periods are scarce, the authors could explore alternative methods to compare model outputs with historical trends.
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Extended Validation Metrics: While the model evaluation provides normalized mean bias (NMB) and normalized mean error (NME) for 2021, these metrics alone may not fully capture the model's performance. I suggest incorporating additional metrics, such as the root mean square error (RMSE) and the correlation coefficient (R), to provide a more comprehensive evaluation. Moreover, the evaluation should be conducted seasonally and include spatial analysis to account for variations in mercury emissions throughout the year. This will help ensure the model's performance across different geographic regions and emission sources.
Specific Comments:
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Definition of GEM (L 247): On line 247, the manuscript introduces "GEM" without defining it. While GEM is a well-known term in mercury studies, it is important to spell out "Gaseous Elemental Mercury (GEM)" upon first use to ensure clarity, especially for readers less familiar with the topic.
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Improving Figure 4: In Figure 4, the authors use a bar plot to compare observed and modeled GEM concentrations. While this provides some insight, a range plot (mean with standard deviation) or a box-and-whisker plot would be a better way to represent the variability in the data. Furthermore, a scatter plot could be added to show the correlation between observed and modeled data points, helping readers assess whether the model accurately captures the distribution of GEM concentrations.
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Clarification of Data Availability (L 294): The manuscript mentions the availability of annual mercury emission inventories on figshare. However, the data in figshare (1978, 1980, 1985, etc.) do not appear to match the continuous data shown in Figure 3. It is important to clarify whether "all annual data from 1978 to 2021" will be made publicly available. If only certain years will be shared, this should be clearly stated in both the manuscript and the data repository to avoid confusion.
Overall, this study has the potential to make a significant contribution to the field, but the reliability of the emission data must be clearly demonstrated.
Citation: https://doi.org/10.5194/essd-2024-252-RC2 -
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