Review of Global source apportionment of aerosols into major emission regions and sectors over 1850–2017.

Overview:



This manuscript investigates the dynamic changes in anthropogenic aerosol concentrations, their sources, and subsequent radiative impacts across four emission regions during different historical periods. This work provides a valuable contribution to our understanding of historical trends in anthropogenic aerosols, emphasizing the importance of regional variations and their implications for both local and global environmental policies. However, there are notable issues, particularly concerning the validation of the model. Addressing these concerns would significantly improve the manuscript, making it suitable for publication. A revised version will provide a nuanced exploration of aerosol dynamics, contributing to the broader discourse on climate change mitigation and collaborative efforts for sustainable environmental management.

Major Comments:

1. In line no. 105, the authors mention the use of the Energy Exascale Earth System Model version 1 (E3SMv1) for their study. However, there is insufficient detail about the model in the introduction. Is E3SMv1 considered a state-of-the-art model for this type of study? What advantages does this model offer over other available global climate models or regional models? The choice of a climate model is crucial in studies of this nature, and the introduction should elucidate why E3SMv1 was deemed suitable for the investigation. A detailed explanation of the model's capabilities, unique features, and any advancements that make it state-of-the-art in the context of this study would enhance the paper's clarity and transparency.
2. Similar question for the source tagging system (E3SMv1-EAST). Is this the standard system for source tagging? What types of systems are used in other models, and why was this system chosen for this study? Source tagging systems play a critical role in attributing aerosol concentrations to their respective emission sources, influencing the accuracy and reliability of the study's findings. Providing background information on EAST's capabilities, its advantages over alternative source tagging systems, and any specific features that make it particularly suited for integration with E3SMv1 would enhance the clarity of the methodology.
3. The entire methodology depends on how accurately the emission inventories are incorporated and how precisely the model predicts PM₅ values compared to observations. However, in the model evaluation section 2.3, lines 151-155, the authors consider observational data from IMPROVE (USA), EMEP (Europe) and CNEMC (China). Observational data from the region of South Asia is not considered, though it is a region where the model study is being run. A major source region of PM_2.5 in South Asia is India and observational data from this region should also be used in the model evaluation, as this region is a part of the model study. Mishra et al. 2021 have shown that Indian region is a major contributor to PM_2.5, even 10 to 20% more than China. With observations limited to China, for both East and South Asia, it becomes challenging to comprehensively assess the model's performance. Considering the region's contribution to PM_2.5, incorporating observational data from South Asia, particularly India, is crucial for a comprehensive assessment of the model's performance.
4. The PM₅ data in Figure 4, panel SAS indicates that India's PM_2.5 mass concentration decreases from 2010-1989 to 2017-2010. However, this contradicts numerous studies (for example: Dey et al 2012, Dey et al 2020, Guttikunda et al 2022, Singh et al 2023) that have found a significant increase in PM_2.5 levels from 2010 onwards. Additionally, the reported PM_2.5 mass concentration values in the range of 4 to 5 μg m^-3 are ten times lower than the values observed by Mishra et al. 2021. This discrepancy raises concerns about the model's performance in the South Asia region, which may cast doubt on the reliability of other results obtained from the model.

Minor Comments:

1. Line 591: Please write the full caption for Figure 5.
2. Line 600: Please write the full caption for Figure 7.

References:

1. Mishra, G., Ghosh, K., Dwivedi, A. K., Kumar, M., Kumar, S., Chintalapati, S., & Tripathi, S. N. (2021). An application of probability density function for the analysis of PM₅ concentration during the COVID-19 lockdown period. Science of the Total Environment, 782, 146681.
2. Dey, S., Di Girolamo, L., van Donkelaar, A., Tripathi, S. N., Gupta, T., & Mohan, M. (2012). Variability of outdoor fine particulate (PM2. 5) concentration in the Indian Subcontinent: A remote sensing approach. Remote sensing of environment, 127, 153-161.
3. Dey, S., Purohit, B., Balyan, P., Dixit, K., Bali, K., Kumar, A., Imam F, Chowdhury S, Ganguly D, Gargava P, & Shukla, V. K. (2020). A satellite-based high-resolution (1-km) ambient PM2. 5 database for India over two decades (2000–2019): applications for air quality management. Remote Sensing, 12(23), 3872.
4. Guttikunda, S., & Nishadh, K. A. (2022). Evolution of India's PM₅ pollution between 1998 and 2020 using global reanalysis fields coupled with satellite observations and fuel consumption patterns. Environmental Science: Atmospheres, 2(6), 1502-1515.
5. Singh, T., et al. (2023). Very high particulate pollution over northwest India captured by a high-density in situ sensor network. Scientific Reports, 13(1), 13201.

Yang et al integrated the Explicit Aerosol Source Tagging (EAST) system into the Energy Exascale Earth System Model version 1 (E3SMv1) to investigate the variations in anthropogenic aerosol concentrations, their sources, and their radiative impacts across four major global emission regions (North America, Europe, East Asia, South Asia) during three key historical periods (1850–1980, 1980–2010, 2010–2017). This research advances our understanding of the historical changes in aerosol pollution, emphasizing the complexity of source-region relationships. The conclusions are primarily derived from simulations performed using this integrated model. However, detailed information about the EAST system within the manuscript is limited. Therefore, I recommend acceptance of the paper after the authors address the following points:

1. The manuscript would benefit from an expanded section on the EAST system. Given the study's reliance on this algorithm for its conclusions, a more in-depth explanation of how and why the EAST system functions is necessary, beyond just referencing previous papers.

2. While the modeled aerosol concentrations align well with 2017 observations from IMPROVE (USA), EMEP (Europe), and CNEMC (China), the study spans a considerable historical period. Therefore, a more robust validation of the modeled data, particularly for earlier periods, would enhance the study's credibility.

3. The use of both column burden and near-surface concentration for discussion is noted. Clarification on the benefits of using column burden in certain contexts would be valuable. Additionally, specifying the defined altitude for 'near surface' in the context of this study would provide clarity.