Wind stress field over the Bohai Sea, the Yellow Sea and the East China Sea

1989 ◽  
Vol 7 (3) ◽  
pp. 240-250 ◽  
Author(s):  
Li Xinming ◽  
Qian Qingying
Keyword(s):  
Stress Field ◽  
East China Sea ◽  
Wind Stress ◽  
Yellow Sea ◽  
Bohai Sea ◽  
The Yellow Sea ◽  
East China ◽  
The East China Sea ◽  
The Bohai Sea ◽  
Wind Stress Field

scholarly journals Detrital Zircon U-Pb Ages in the East China Seas: Implications for Provenance Analysis and Sediment Budgeting

Minerals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 398
Author(s):  
Xiangtong Huang ◽  
Jiaze Song ◽  
Wei Yue ◽  
Zhongbo Wang ◽  
Xi Mei ◽  
...  
Keyword(s):  
East China Sea ◽  
Yellow Sea ◽  
Bohai Sea ◽  
East China ◽  
China Seas ◽  
Provenance Analysis ◽  
The East China Sea ◽  
The Bohai Sea ◽  
China Sea ◽  
Source Regions

Linking marine sinks to potential terrestrial sources is one of most intriguing but challenging aspects of sediment source-to-sink studies. In this study, we analyzed 23 zircon samples (3271 filtered best ages) from surface sediments of the east China seas (ECSs) that cover a large portion of the Bohai Sea, Yellow Sea, East China Sea to part of the northeastern South China Sea. The results of U-Pb age distributions exhibit variable signatures in different seas. The Bohai Sea is characterized by 4 age populations at 203–286 Ma, 383–481 Ma, 1830–1940 Ma and 2480–2548 Ma, whereas the southern Yellow Sea and the East China Sea are featured by 5 age populations at 176–223 Ma, 383–481 Ma, 732–830 Ma, 1830–1940 Ma and 2480–2548 Ma. We propose that the presence or absence of the population of 732–830 Ma in the Yangtze Craton (YC) and the North China Craton (NCC) is a possible geochronological signature to distinguish zircon grains derived from the two source regions. Furthermore, on the basis of multidimensional scaling (MDS), U-Pb ages in the sediments of the Bohai Sea, East China Sea and the Taiwan Strait could be correspondently linked to those of the Yellow River, the Yangtze River and Taiwan rivers. The good linkages support the view that U-Pb age distributions of detrital zircons in the margin seas are mainly controlled by fluvial discharges, and ultimately, by the tectonic history of the corresponding source regions. Using a sediment forward mixing model, we obtained the relative sediment contributions and spatial variations of five most important river discharges in the region. The mixing results suggest that the major rivers in the region, i.e., the Yangtze and the Yellow Rivers, are the dominant sediment contributors to the continental margin, and their mixing coefficients could be used to infer relative sediment budgeting. In addition, spatial variations in mixing coefficient in the East China Sea indicate that sediment mixing and partitioning processes in the marine depositional environment have played a part role in propagating the provenance signals as a result of interaction of oceanic currents and tides. The combined method between provenance analysis and mixing modeling provides a feasible way to appreciate sediment budgeting in the geological past.

scholarly journals Mapping gaseous amines, ammonia, and their particulate counterparts in marine atmospheres of China's marginal seas: Part 2 – spatiotemporal heterogeneity, causes and hypothesis

2021 ◽  
Author(s):  
Yating Gao ◽  
Dihui Chen ◽  
Yanjie Shen ◽  
Yang Gao ◽  
Huiwang Gao ◽  
...  
Keyword(s):  
East China Sea ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Chemical Conversion ◽  
The Yellow Sea ◽  
East China ◽  
Marine Atmosphere ◽  
The East China Sea ◽  
China Sea ◽  
Marginal Seas

Abstract. In this study, spatiotemporal heterogeneities in the concentrations of alkaline gases and their particulate counterparts in the marine atmosphere over China's marginal seas were investigated in terms of causes and chemical conversion during two winter cruise campaigns, using semi-continuous measurements made by an onboard URG-9000D Ambient Ion Monitor-Ion chromatograph (AIM-IC, Thermofisher). During the cruise campaign over the East China Sea on December 27, 2019–January 6, 2020, the concentrations of atmospheric trimethylamine (TMAgas) varied by approximately one order of magnitude, with an average (±standard deviation) of 0.10 ± 0.04 µg m−3. Corresponding means were 0.037 ± 0.011 µg m−3 over the Yellow Sea on 7–16 January 2020 and 0.031 ± 0.009 μg m−3 over the Yellow Sea and the Bohai Sea on 9–22 December 2019. In contrast, the simultaneously observed concentrations of TMA in PM2.5, detected as TMAH+, over the East China Sea were 0.098 ± 0.068 µg m−3 and substantially smaller than 0.28 ± 0.18 μg m−3 over the Yellow Sea and the Bohai Sea on 9–22 December 2019. A significant correlation between TMAgas and particulate TMAH+ was obtained over the East China Sea, but no correlation existed over the Yellow Sea and Bohai Sea. The proportional or disproportional variations in concentrations of TMAgas with particulate TMAH+ over the sea zones were likely attributed to the difference in enrichment of TMAH+ in the sea surface microlayer. In addition, spatiotemporal heterogeneities in concentrations of atmospheric ammonia (NH3gas), atmospheric dimethylamine (DMAgas), and DMA in PM2.5, detected as DMAH+, were also investigated. Case analyses were performed to illustrate the formation and chemical conversion of particulate aminium ions in marine aerosols. Finally, we hypothesized a release of basic gases and particulate counterparts from the ocean to the atmosphere, together with secondary formation of DMAH+ and chemical conversion of TMAH+, in the marine atmosphere.

Distribution and influencing factors of dissolved manganese in the Yellow Sea and the East China Sea

2021 ◽  
pp. 104002
Author(s):  
Yuan Zhang ◽  
Lei Li ◽  
Jingling Ren ◽  
Huijun He ◽  
Ruifeng Zhang ◽  
...  
Keyword(s):  
Influencing Factors ◽  
East China Sea ◽  
Yellow Sea ◽  
The Yellow Sea ◽  
East China ◽  
The East China Sea ◽  
China Sea

scholarly journals Summer and winter living coccolithophores in the Yellow Sea and the East China Sea

2014 ◽  
Vol 11 (3) ◽  
pp. 779-806 ◽  
Author(s):  
J. Sun ◽  
X. Y. Gu ◽  
Y. Y. Feng ◽  
S. F. Jin ◽  
W. S. Jiang ◽  
...  
Keyword(s):  
Surface Layer ◽  
Environmental Factors ◽  
East China Sea ◽  
Correspondence Analysis ◽  
Yellow Sea ◽  
The Yellow Sea ◽  
East China ◽  
The East China Sea ◽  
China Sea ◽  
Living Coccolithophores

Abstract. This paper describes the distribution of living coccolithophores (LCs) in the Yellow Sea and the East China Sea in summer and winter, and its relationship with environmental factors by canonical correspondence analysis (CCA). We carried out a series of investigations on LCs distribution in the Yellow Sea and the East China Sea in July and December 2011. 210 samples from different depths were collected from 44 stations in summer and 217 samples were collected from 45 stations in winter. Totally 20 taxa belonging to coccolithophyceae were identified using a polarized microscope at the 1000 × magnification. The dominant species of the two seasons were Gephyrocapsa oceanica, Emiliania huxleyi, Helicosphaera carteri, and Algirosphaera robusta. In summer the abundance of coccolithophore cells and coccoliths ranged 0–176.40 cells mL−1, and 0–2144.98 coccoliths mL−1, with the average values of 8.45 cells mL−1, and 265.42 coccoliths mL−1, respectively. And in winter the abundance of cells and coccoliths ranged 0–71.66 cells mL−1, and 0–4698.99 coccoliths mL−1, with the average values of 13.91 cells mL−1 and 872.56 coccoliths mL−1, respectively. In summer, the LCs in surface layer were mainly observed on the coastal belt and southern part of the survey area. In winter, the LCs in surface layer had high value in the continental shelf area of section P. The comparison among section A, section F, section P and section E indicated lower species diversity and less abundance in the Yellow Sea than those in the East China Sea in both seasons. Temperature and the nitrate concentration may be the major environmental factors controlling the distribution and species composition of LCs in the studying area based on CCA. Abbreviations: LCs: Living Coccolithophores; CCA: canonical correspondence analysis; DCM: Deep Chlorophyll Maximum

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