the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A dataset of high–spatiotemporal–resolution dust and non–dust aerosol mass concentration profiles from ground–based remote sensing in central China (2018–2024)
Abstract. The atmospheric aerosol composition and vertical structure over eastern China are jointly influenced by mineral dust transport, anthropogenic emissions, and boundary layer processes; however, long–term vertically resolved observations that distinguish dust from non–dust aerosol mass remain limited. Here, we present a quality–controlled dataset of dust, non–dust, and total aerosol mass concentration profiles derived from continuous ground–based polarization lidar observations at the Shouxian National Climate Observatory (32.434° N, 116.793° E) in central China from January 2018 to December 2024. The site lies in a regional transport corridor between the Beijing–Tianjin–Hebei and Yangtze River Delta regions and is therefore well–suited for monitoring aerosol exchange between two major source–receptor systems in eastern China. Raw lidar measurements were screened for clouds, precipitation, and instrumental artifacts, harmonized to 15 min and 7.5 m resolution, and processed with the Fernald inversion and Polarization lidar photometer networking (POLIPHON) framework to retrieve vertically resolved dust and non–dust aerosol mass concentrations from approximately 0.3 to 6 km. Monthly extinction–to–mass conversion factors were constrained using collocated CE–318 sun–photometer observations. The product was evaluated against independent column, profile, and near–surface references, including CE–318 aerosol optical depth, CALIPSO aerosol extinction and depolarization profiles, and surface particulate matter measurements from a colocated Grimm monitor. The comparisons indicate that the dataset reproduces the main temporal variability of column aerosol loading, captures the principal vertical structure of aerosol extinction coefficient, and is statistically consistent with near–surface particulate matter variability. A pointwise retrieval quality index (RQI) ranging from 0 to 100 is provided to facilitate data screening and reuse, with RQI values of 75–100 recommended for quantitative analyses and event studies, 50–75 for routine statistical analyses, and lower values mainly for qualitative interpretation or analyses based on temporal or vertical averaging. This dataset provides a multi–year, high–resolution observational record of aerosol type–resolved mass profiles in central China and is intended to support community reuse in satellite validation, model evaluation, aerosol transport studies, boundary layer research, and data assimilation applications. The dataset is available for free at Mendeley Data (https://doi.org/10.17632/kfvpbb3vcs.1; Wang et al., 2026).
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Status: open (until 09 Dec 2026)
- RC1: 'Comment on essd-2026-677', Anonymous Referee #1, 26 Oct 2026 reply
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A dataset of high–spatiotemporal–resolution dust and non–dust aerosol mass concentration profiles from ground–based remote sensing in central China (2018–2024) Wang, Z. Chen, Y. https://doi.org/10.17632/kfvpbb3vcs.1
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- 1
Goal of the paper is to document the pollution (non-dust) and dust conditions in the vertical profile in eastern China from 2018-2024. The study is based on ground-based lidar observations at a site in central China. Of key importance for the data analysis is the capability of the lidar to separate the dust and pollution aerosol components by using the measured particle linear depolarization ratio (PLDR).
Unfortunately, the measured PLDR profiles are rather questionable. In Figure 3, comparisons between the Chinese ground-based lidar and the well validated spaevborne CALIPSO lidar observation of PLDR are presented. A strong bias is visible. For example, the 2018 annual mean PLDR is less than 5% in the case of the ground-based lidar and around 15% in the case of the CALIPSO lidar. As a consequence, the dust fraction is almost negligible when measuring 5% and contributes to roughly 50% to the aerosol burden when measuring a PLDR of 15%. And such a strong bias is visible for all analyzed years except maybe 2019 where the bias is small.
There are many papers that clearly show the high-quality PLDR observations by the CALIPSO lidar. Recently, Proestakis et al. (ACP, 2025, acp-25-14777-2025) presented Beijing observations with the CALIPSO lidar (in their Figure 1).
In conclusion, because of the strong PLDR bias in Figure 3, I can only recommend to reject the manuscript. All results in Figures 4-10 are based on the PLDR profiles measured with the Chinese lidar.