| 22 Jul 2026
Surface sediment grain-size characteristics of the Bohai, Yellow, and East China seas: a comprehensive survey based on unified laser diffraction analysis
Abstract. This paper presents a comprehensive grain-size dataset comprising 751 surface sediment samples collected from the Bohai Sea, Yellow Sea, and East China Sea—the three major continental shelf seas of China. Samples were obtained over an extended period from 2001 to 2019 using box samplers in accordance with the national marine survey standard (GB/T 12763.8–2007). All samples were analyzed by a Malvern Mastersizer 2000 laser diffraction particle size analyzer following hydrogen peroxide pretreatment for organic matter removal.
The dataset provides the full grain-size distribution at 0.25 Φ intervals from −0.75 Φ to >10 Φ. The instrument measures directly in micrometres (0.02–2000 μm); we provide two companion data files — one in Φ (phi) scale and one in micrometres (μm) — to accommodate both sedimentological convention and direct instrumental units. The dataset also includes derived Folk–Ward statistical parameters including mean grain size (Mz), sorting coefficient (σi), skewness (Ski), and kurtosis (Kg). Ternary fractions of sand (−1 to 4 Φ), silt (4 to 8 Φ), and clay (>8 Φ) are also included.
Across the dataset, sand content ranges from 0 to 100 % (mean 40.1 %), silt from 0 to 76.8 % (mean 41.3 %), and clay from 0 to 50.3 % (mean 18.6 %). Mean grain size (Mz) ranges from 1.51 to 8.13 Φ (mean 5.26 Φ, corresponding to approximately 26 μm). The dataset is expected to support research on sediment dynamics, paleoenvironmental reconstruction, benthic habitat mapping, and marine engineering across the Chinese continental margin. All data are archived in an open-access repository with a CC BY 4.0 license.
This paper presents a compilation of seafloor sediment grain size data from the east China continental shelf. The samples were collected by box corer over a period of 19 years, and analyzed using a laser diffraction particle size analyzer. The resulting data were characterized using a range of sediment parameters, and are described spatially. The analysis methodology was highly consistent for the entire dataset. The data are provided in an accessible format (.xlsx) that can be universally read, with clear documentation.
General comments
Generally, I think this is a straightforward presentation of a well-curated dataset. I have few comments about the content of the paper or the data – it seems like a fantastic resource. I downloaded the data, and from a glance, everything seems to be in order. The methods are well-described, and the writing is clear. Possibly the greatest strength of the dataset is the consistency with which the samples were collected and processed. This is uncommon for such a comprehensive grain size dataset collected over such a long period of time.
One general question: I am curious whether the authors believe there are any drawbacks of using the particle size analyzer compared to, say, sieving? Do you lose characterization of coarser fractions?
I think some improvement of the map figures is advisable. See specific comments below.
Specific comments
Line 113. Could you state the concentration of sodium hexametaphosphate so the methods are entirely documented and reproduceable?
Lines 122-127. Can you explain the selection of graphical parameters over other options, since you have the full CDF from the particle size analyzer?
Lines 175-182, Figure 3. How were these spatial distribution maps produced? Was it interpolation? Some description should be provided. This is especially relevant for the % composition maps, because interpolation of compositional data requires some additional considerations (see Aitchison, 1982; Lark et al., 2012; Stephens & Diesing, 2015).
Figure 3, 4. The quality of these maps could be improved. There are some cartographic conventions that are not critical, but that you could choose to implement, such as adding a scale, using sans serif font, and selecting a unidirectional or lightness-based color palette (which are colour blind-friendly). I would also note that the rivers in Figure 4 are the same colour as some of the mapped values. It is apparent from reading the paper that you did not map the rivers, but it is generally a good idea to use different symbology to eliminate any potential misinterpretation. Other issues should be addressed because they inhibit readability and interpretation: there is inconsistent font sizing between maps, with some text (e.g., Figure 3) too small and low-resolution to read without zooming way in. It seems there are tiny low-resolution contour labels of specific mapped values on the maps. These are essentially not interpretable, and in the case of Fig 3c, are not readable at all.
Technical corrections
Some spacing issues remain, for example in line 23.
References
Aitchison, J., 1982. The statistical analysis of compositional data. Journal of the Royal Statistical Society. Series B (Methodological) 44, 139–177.
Lark, R.M., Dove, D., Green, S.L., Richardson, A.E., Stewart, H., Stevenson, A., 2012. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations. Sedimentary Geology 281, 35–49. https://doi.org/10.1016/j.sedgeo.2012.07.009
Stephens, D., Diesing, M., 2015. Towards quantitative spatial models of seabed sediment composition. PLOS ONE 10, e0142502. https://doi.org/10.1371/journal.pone.0142502