the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Dec 2024
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019–2023)
Abstract. Vertical profile observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profile data. This study introduces a high-time-resolution (approximately 15 minutes) dataset of vertical profile observations of atmospheric composition (aerosol, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal variations and diurnal pattern of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing would facilitate the scientific community in exploring of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate and feedback mechanisms. It also holds potential for enhancing satellite retrieval methods and advancing the development of regional transport models. The dataset is available for free at Zenodo (https://doi.org/10.5281/zenodo.14194965; Jiao et al., 2024).
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RC1: 'Comment on essd-2024-562', Anonymous Referee #1, 27 Jan 2025
General comments:
Jiao et al. present a comprehensive dataset of vertical profile observations of aerosol, NO2, and HCHO in China from 2019 to 2023 using a hyperspectral remote sensing network. The dataset fills a critical gap in vertical profile monitoring, providing high temporal resolution and wide geographic coverage. The manuscript is well-organized and offers valuable insights into the spatial-temporal variations of atmospheric components, which can inform environmental policies and enhance scientific modeling. However, there are several areas where the manuscript could be improved to enhance clarity, accuracy, and impact.
Specific comments:
- Section 2.2 mentions data filtering criteria (e.g., DOF, relative error thresholds), but the rationale behind these criteria needs to be explained in more detail. In addition, it is recommended to include a specific site example to demonstrate the differences in data distribution before and after filtering.
- In Section 3.4 (Validation), the manuscript shows good correlations between the dataset and CNEMC and TROPOMI data. However, there is a lack of a detailed discussion on the potential biases and uncertainties in these comparisons. For example, how do the differences in spatial and temporal resolution between MAX-DOAS and TROPOMI contribute to the observed discrepancies?
- While the dataset covers seven major regions, monitoring sites are fewer in regions such as Central and Southwest China. The manuscript should discuss how this uneven distribution might impact regional representativeness and the generalizability of the results.
Technical comments:
Line 26.
‘Its sharing would facilitate the scientific community in exploring of source-receptor relationships’ → ‘Its sharing would facilitate the scientific community in exploring source-receptor relationships’.
Line 42.
‘Aerosol, as one of the most complex and critical composition of the atmospheric environment’ → ‘Aerosol, as one of the most complex and critical compositions of the atmospheric environment’.
Line 98.
‘It helps provide a complete perspective on the vertical distribution of aerosol, NO2, and HCHO in China’ → ‘The diversity of these monitoring sites helps provide a complete perspective on the vertical distribution of aerosol, NO2, and HCHO in China’.
Line 108.
‘…which located in China’s economically developed and densely populated areas’ → ‘…which are located in China’s economically developed and densely populated areas’.
Line 110.
‘offer vertical distribution data across different elevation’ → ‘offer vertical distribution data across different elevations’.
Line 223.
‘AECs in spring, summer, autumn, and winter accounts for 23.60%, 24.63%, 24.69%, and 27.08% of the total averaged values of four seasons, respectively’ → ‘AECs in spring, summer, autumn, and winter account for 23.60%, 24.63%, 24.69%, and 27.08% of the total averaged values of four seasons, respectively’.
Line 237.
‘High-concentration aerosol with extinction coefficients exceeding 1.0 km⁻¹ are primarily distributed below 600 m, while aerosol with extinction coefficients greater than 0.6 km⁻¹ are concentrated below 1000 m’ → ‘High-concentration aerosols with extinction coefficients exceeding 1.0 km⁻¹ are primarily distributed below 600 m, while aerosols with extinction coefficients greater than 0.6 km⁻¹ are concentrated below 1000 m’.
Line 240.
‘with one peak occurring before 12:00 BJT and the other between 16:00 and 18:00 BJT’ → ‘with one peak occurring before 12:00 Beijing Time (BJT) and the other between 16:00 and 18:00 BJT’
Line 243.
‘located in the central Beijing’ → ‘located in central Beijing’.
Line 287.
‘The averaged near-surface NO2 concentrations in spring, summer, autumn, and winter accounts for 23.06%, 16.57%, 25.74%, and 34.63% of the total averaged values of four seasons, respectively’ → ‘The averaged near-surface NO2 concentrations in spring, summer, autumn, and winter account for 23.06%, 16.57%, 25.74%, and 34.63% of the total averaged values of four seasons, respectively’.
Line 310.
‘transportation’ → ‘transportation from’.
Citation: https://doi.org/10.5194/essd-2024-562-RC1 -
RC2: 'Comment on essd-2024-562', Anonymous Referee #2, 07 Feb 2025
This manuscript presents a high-time resolution dataset of vertical profile of aerosol, NO2 and HCHO across 32 sites in seven major regions of China from 2019 to 2023, which obtained from the hyperspectral vertical remote sensing network in China. It provides a comprehensive analysis of the patterns of the vertical distribution, seasonal variations and diurnal pattern of these pollutants, and comparisons with the CNEMC stations and the TROPOMI satellite data were conducted and the quality of the present dataset was analyzed. This work is quite challenging, not only because of the complexity of the observation environment, for example, the observation sites include both the Tibetan Plateau and the coastal zone; at the same time, the stability and reliability of the MAX-DOAS work in different observation environments also bring challenges to the data retrievals. Whatever, the good agreement with TROPOMI satellite and ground-based CNEMC measurements showed the data quality is assured.
The dataset it provides could be useful for future scholars in the fields of atmosphere environment and climate change. Overall, this is a good paper that deserves to be published in ESSD. Nevertheless, some minor issues must be clarified.
First, the information about the 32 sites is limited, more description should be provided, including the major emission sources around the each site and the site type (urban, suburban, background etc.) and observation period in each site suggested to be added in Table 1. It is important to understand the seasonal and diurnal pattern of the observed pollutants.
Second, as the vertical dataset covers the period of 2019-2023, which significant improvements in air quality and decrease of air pollutants at ground had been reported, it would be interesting to provide the analysis of temporal changes of the observed pollutants (aerosol, NO2 and HCHO) in the upper atmosphere.
Finally, discussion about the diurnal pattern of HCHO should be re-examined. For example, the evening peak of HCHO at the UCAS site was much enhanced than the IAP site (Figure 10), apparently, it could not be due to vehicular emissions during evening rush hours, as the former received much more traffic emissions (Figure 8).
Citation: https://doi.org/10.5194/essd-2024-562-RC2
Data sets
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019–2023) Peiyuan Jiao, Chengzhi Xing, and Cheng Liu https://doi.org/10.5281/zenodo.14194965
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