The structures in the riverbed or seabed may be affected or damaged by emergency anchoring. In the channel with frequent navigation, the emergency anchoring penetration depth need to be fully considered during the design of the buried depth of structures in riverbed. In this paper, small scale model tests are carried out to simulate the process of anchoring penetration of hall anchor in silty sand, and the influences of different impact velocities (1.15−4.4 m/s) and relative density of silty sand (0.45−0.65) on anchoring penetration depth are investigated. Based on the Terzaghi ultimate bearing capacity theory and the energy conservation law, the expression of penetration depth of the hall anchor in silty sand is deduced, and the theoretical calculation results are conservative compared with experimental results, the revised results of the theoretical calculation results are in good agreement with experimental data. The research results can provide a reference for the design of buried depth of structures in silty sand riverbed or seabed.

The identification of abnormal marine ecological buoy data is the key to ensure the quality of buoy data. In this study, we found that the gradual abnormal data type is different from the traditional jump abnormal data through analysis of the coastal buoy data in Zhejiang for many years. With a single parameter analysis method, it is difficult to work out accurately the new gradual abnormal data type of stable and gradual deviation from the normal data. Therefore, multiple parameters correlation coefficient method is proposed based on the relationships between pH, dissolved oxygen and chlorophyll a on the condition of that the correlation between two parameters is stable or even consistent at a certain time series. There are two simple statistical parameters of the cross-correlation coefficient of 8-day time window (R_{8 d}) and the difference of R_{8 d} (ΔR) in this method. Those could be used to automatically detect the gradual abnormal buoy data and do very well. The multiple parameters correlation coefficient method provides a new idea for the gradual abnormal data identification, and also improves the automatic monitoring capability of marine ecological buoy abnormal data.

The Changjiang River Delta coastal zone is a typical region where global change and human activities interact violently. Under the background of deepening coastal development and policy boosting, land use changes in this region has intensified, and it has become a conflict hotspot between economic development and ecological protection. The evolution analysis of land use pattern is of great significance to promote the coastal development zone of the Changjiang River Delta. The article is based on 5 issues of land use vector data during 1980 to 2018, by using Sankey diagram, annual change rate, dynamic degree and nuclear density analysis method to reveals the evolution characteristics of land use in the Changjiang River Delta coastal zone in the past 39 years in terms of quantitative structure, spatial agglomeration of key land types, and the rule of sea-land gradient variation. The results are as follows: (1) The basement landscape of the Changjiang River Delta coastal zone in 2018 is composed by cultivated land, forest land and construction land, which occupies 89.86% of the total study area. In spatial pattern, it presents cultivated land in the north and woodland in the south and construction land is scattered and embedded in it. (2) In the past 39 years, the land use system in the study area has varied in quantitative structure and spatial pattern. Among them, in the overall presentation of quantity structure, the cultivated land continued to decline steadily, construction land continues to increase rapidly, forest land fluctuates slightly, and grasslands and waters fluctuate slightly, and the total area which experienced reclamation development reached to 2161.01 square kilometer; In spatial pattern, it mainly shows that construction land patches blooming everywhere and accompanied by local explosive expansion, farmland patches shrink gradually and tend to fragment, and form a distinct wetland landscape in reclamation areas in Jiangsu and eastern Shanghai and Hangzhou Bay. (3) The article focus on the spatial agglomeration analysis on construction land and wetland. Among them, the expansion scale of construction land is small and scattered, and the expansion concentration degree continues to increase, which forms a spatial clustering pattern from "single center" to "multiple center"; The tidal flat resources are characterized by a small number, small patches and scattered distribution, and generally show a decreasing trend. The expand and reduce hotspots in different periods behaves differently in different regions. (4) Within a distance of 20 kilometers from the coastline, the land use dynamics in the study area showed obvious changes of construction land goes to sea and ocean land goes to land, in addition, the dynamics of sea-land gradients of different types in the coastal areas of Jiangsu, Shanghai and Zhejiang are different.

Surface sediments were collected by high-resolution sampling from the Changjiang River Estuary and its adjacent shelf in March, 2014. Grain size composition, Specific Surface Area (SSA), Total Organic Carbon (TOC) and stable carbon isotope composition (δ¹³C), n-alkanes and its related indices in sediments were analyzed to discuss the high-resolution distribution patterns of sedimentary organic carbon (OC) and n-alkanes in this region. A three end-members mixing model based on Principal Component Analysis (PCA) and Monte-Carlo Simulation was constructed to characterize the sources of sedimentary OC quantitatively. The results showed that TOC contents were 0.45%±0.16%, which were relatively higher in coastal mud area than offshore sandy area. Absolute contents (Σn−Alk) and relative contents (Σn−Alk/TOC) of total n-alkanes (C₁₄ to C₃₅) were (1.42±0.73) μg/g and (0.34±0.21) mg/g , respectively. There was a strong odd to even carbon preference of long-chain n-alkanes in muddy sediments, while there was an even to odd carbon preference of short-chain n-alkanes in sandy area. Inputs from the Changjiang River, the Old Yellow River Estuary and small rivers in the Zhe-Min coasts and hydrodynamic sorting restricted the transport and dispersal patterns of n-alkanes. The results of the three end-members mixing model indicated a mixture input of marine, soil and higher plant derived OC in this region. Among them, sedimentary OC was dominated by marine source (42.70%±18.18%), increasing from coast to outer sea gradually. Contributions of soil OC and higher plant OC were 28.99%±15.37% and 28.31%±17.12%, respectively. Influenced by hydrodynamic forces, obvious differentiation of these two terrestrial OC pools occurred during transport after entering into ocean. Soil OC was mainly associated with fine grains, and was transported southward along the Zhe-Min coast, while higher plant derived OC was mainly transported along northeast direction in the Changjiang River Estuary.

The Bering Sea and western Arctic Ocean, as high production areas, play a key role in the Arctic Ocean biological pump, and are vulnerable to abrupt climate change, especially sea water warming and sea ice melt. Alterations in biological pump can influence the sources and degradation of sedimentary organic matter, and thus can be indicated by fatty acid (FA) content and composition of sediment. FA analysis of surface sediments, collected during the 5^th and 6^th Chinese Arctic Research Expeditions, showed that the total FA of the Chukchi Shelf was exceptionally high ((97.15 ± 55.31) μg/g), while the Bering Basin was the lowest ((15.00 ± 1.3) μg/g), and the Canada Basin, the Chukchi Shelf and the Bering Shelf were intermediate ((88.65 ± 3.52) μg/g, (70.35 ± 11.32) μg/g and (38.28 ± 14.89) μg/g, respectively). Marine FAs (short chain saturated FA + unsaturated FA) accounted for the most abundant (86.82% ± 7.08%), terrestrial FAs (long chain saturated FA) as the second abundant (8.45% ± 6.62%), while bacterial FAs (odd FA) as the least (4.63% ± 2.24%); diatom index (16:1ω9/16:0) was high at the southern and northern Chukchi Shelf (> 0.82) and the Bering Shelf edge (> 0.65), while it was low at the rest areas. These results indicated that: (1) marine source was the major contributor of sedimentary organic matter of the Bering Sea and the western Arctic Ocean, while terrestrial one contributes minor; diatom predominates was the primary producers of the southern and northern Chukchi Shelf and the Bering Shelf edge; percentage of bacterial FAs was remarkably low, comparing with tropical and temperate seas, suggesting a suppressed bacterial activity under low temperature; (2) labile organic matter accumulation rate was extremely high at the Chukchi Shelf, and was extremely sensitive to sea water warming and sea ice melt; (3) chlorophyceae and prymnesiophyceae dominate phytoplankton community at the Canada Basin and the Chukchi Slope. In conclusion, FA of surface sediment can be used to indicate sources and degradation of organic matter in the Bering Sea and the western Arctic Ocean; further, combining with other samples and biomarkers, FA was viable to shed light on the response of biological pump under the abrupt Arctic climate change.

The n-alkanes and fatty acids were analyzed in two sediment cores at the upwelling edge region (Station S5) and center area (Station S10) of eastern Hainan Island, the source and degradation characteristics of organic matter were comprehensively analyzed by combining organic carbon content, particle size, carbon stable isotope (δ¹³C) and other parameters. The interannual variation of upwelling intensity in the study area was reconstructed using the diatom abundance parameter ∑16∶∑18 of fatty acids. The grain size results show that Station S10 is located near the center of upwelling, with coarse grain size and mainly sand; Station S5 is located at the edge of upwelling, with fine grain size and mainly silt. The δ¹³C and fatty acids parameter (∑C_20-:0/∑C_20+:0) of the two stations show that both stations are dominated by marine source input. The degradation degree of organic matter in Station S10 is greater than that in Station S5 . The diatom abundance parameters of fatty acids and the percentage of phytoplankton fatty acid indicate the primary productivity of Station S10 is higher than Station S5, and ∑C₁₆∶∑C₁₈ is used to indicate the interannual variation of the intensities of the upwelling in the study area; the upwelling intensity shows weak, strong, and weak signals between 1925−1950, 1950−1980, and 1980−2008. It is consistent with the interdecadal trend of Pacific Decadal Oscillation. El Niño-Southern Oscillation may regulate the upwelling intensity in a short time scale, while in a long time scale, it may be mainly regulated by Pacific Decadal Oscillation.

As one of the forms of land-ocean interactions, submarine groundwater discharge (SGD) can release solutes into the coastal sea and has a significant impact on the nutrients budget in coastal seawater. Here, using ²²²Rn tracer, the SGD and the associated dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) inputted to the Zhenzhu Bay, a typical mangrove-dominated bay, were quantified. The results show that the average concentrations of ²²²Rn, DIC and DOC in groundwater are relatively higher than those in river water and surface sea water. A ²²²Rn mass balance implies that SGD rate is (0.36±0.36) m/d during January 2019. And SGD-derived DIC and DOC fluxes are estimated to be (2.41±2.63)×10⁷ mol/d and (1.96±2.20)×10⁶ mol/d. It confirmed that SGD-derived carbon is the most important carbon source in this bay, with 91% DIC and 89% DOC of the total input fluxes by SGD, respectively. Our results highlight the importance of groundwater-derived carbon fluxes in the Zhenzhu Bay, especially in the blue carbon assessments and biogeochemical process in tidal zones such as mangrove ecosystems.

⁹⁰Sr is recognized to be one of most important artificial radionuclides. A huge amount of radioactive substance (e.g., ⁹⁰Sr) was released into marine environment after the Fukushima Nuclear Accident (FNA). High ⁹⁰Sr activity was still observed in the treated wastewater which was stored on site in many tanks. However, ⁹⁰Sr was rarely investigated in marine environment due to its complicated and time-consuming analytical procedure after the FNA, constraining a comprehensive understanding of the fate of ⁹⁰Sr in marine environment. We discussed the ⁹⁰Sr activity and environmental half-life (EHL) in seas surrounding Japan (SSJ) and the South China Sea (SCS) on the basis of previous data and our ⁹⁰Sr data in seawater and marine biotas (e.g., sargassum, shrimp, oyster, mangrove, reef coral) collected from the SCS during 2015−2018. We found that the EHL of ⁹⁰Sr in the SSJ was 15.4 years during 1975−2010. ⁹⁰Sr in the SSJ was significantly elevated after the FNA. Radiation dose rate of ⁹⁰Sr on marine fish was increased by five orders of magnitude after the FNA relative to the baseline before the FNA. As the downstream basin of the North Pacific Subtropical Gyre, the SCS was not identified with noticeable ⁹⁰Sr-derived from the FNA. The EHL of ⁹⁰Sr in the SCS was calculated to be 26.7 years during 1984−2018 based on the compilation of historical ⁹⁰Sr data. The contrasting patterns of the EHL of ⁹⁰Sr and ¹³⁷Cs in the marginal seas and open oceans were attributed to the distinct sources (river input) and sinks (marine biological pump) of ⁹⁰Sr and ¹³⁷Cs. In the context of challenge of ⁹⁰Sr analytical method in marine environment, extremely high ⁹⁰Sr concentration factor (around 1000 L/kg) was observed in the reef coral skeleton after comparing with concentration factors of ⁹⁰Sr in more than 10 kinds of marine biotas. Additionally, reef coral is potential to be a reliable ⁹⁰Sr marine bioindicator with other advantages of easy availability in large quantity, growth at a fixed location, continue record with high resolution, and simple pretreatment. The study of ⁹⁰Sr in reef coral will not only help to reveal distinct sources and sinks of artificial radionuclides in marine environment, but also provide valuable insights to optimization and improvement of standards/guidelines of marine radioactivity monitoring program.

The metal contents in the core sediments collected from the Daya Bay, a typical marine environment in the Guangdong-Hong Kong-Macao Greater Bay Area were measured in this study. National standard for marine sediment quality and fuzzy comprehensive assessment were applied to evaluate the historical evolution of the sediment quality in the past one hundred years. Our results indicate that the metal contamination in the bay experienced three major stages: (1) Before 1980s, the concentrations of most metals were low and the sediment quality fell into the category of class I. Accordingly, the metal risk was low in this period. Cr contributed most and Cd contributed least to the risk, respectively. (2) Between 1980 and 2000, the metal contents were significantly higher than the background values, suggesting that there was a sharp increase of metal input. The category of sediment quality fell down to class II and class III. However, the sediments in the margin areas of the Daya Bay were only slightly affected and still belonged to class I quality. Cu acted as a major metal pollutant instead of Cr in this period. (3) After the year of 2000, the metal levels and the metal risk decreased sharply. The sediment quality recovered back to class I and Cr contributed most to the risk at the time.

Sea surface partial pressure of carbon dioxide (pCO₂) is a crucial parameter for estimating ocean carbon source and sink term, but its sparse and uneven in situ measurements in space and time lead to large uncertainty in the estimate of sea-air CO₂ flux and characteristics of ocean carbon source and sink. To eliminate this uncertainty, a general regression neural network approach using the Surface Ocean CO₂ Atlas (SOCAT) dataset, based on the non-liner regression of pCO₂ and longitude, latitude, time, temperature, salinity and concentration of chlorophyll, was successfully used in the reconstruction of global 1°×1° resolution monthly sea surface pCO₂ from 1998 to 2018, with a root mean square error (RMSE) of 16.93 μatm and a mean relative error (MRE) of 2.97%, lower than existing feed-forward neural network (FFNN), self-organizing neural network (SOM) and machine learning approaches. The global distribution of pCO₂ obtained by this approach agrees well with existing researches.