Spatial distribution and modelling of 239+240Pu in the sediments and seawater columns of the South China Sea and Indian Ocean

In order to investigate the 239+240Pu potential influence in the ocean, and develop a new method for rapidly monitoring radioactive pollution, the 239+240Pu spatial distribution in the South China Sea (SCS) and the Indian Ocean (IND) sediments is analyzed by SF-ICP-MS (ELEMENT 2). The inventory-weighted mean activities of 239+240Pu were 0.413 ± 0.333 mBq/g, 0.128 ± 0.044 mBq/g, and 0.483 ± 0.606 mBq/g in the sediments of the SCS, eastern IND, and Arabian Sea, respectively. The 239+240Pu activity spatial distribution in the SCS sediments was influenced by the current, the vertical distribution of Pu in seawater, and the transport of particulate matter. The 239+240Pu activity spatial distribution in the IND sediments could be impacted by Antarctic Intermediate Water. The average of 240Pu/239Pu atomic ratios were 0.258 ± 0.034, 0.219 ± 0.031, and 0.212 ± 0.028 in the sediments of the SCS, eastern IND, and Arabian Sea, respectively. The 240Pu/239Pu atomic ratios in the SCS and IND indicate that Pu from the Pacific Proving Ground (PPG) is transported to the IND via the SCS internal current and transverse ocean currents within Indonesia. In addition, a seawater advection-dispersion equation (S-ADE) model is established based on the actual physical processes of radionuclides in the seawater column and well fitting results were obtained (R2 = 0.49 to 0.99). The 239+240Pu data and the geographic information from the sample site were used to correct the Pu distribution in the seawater. The calculated 239+240Pu mean concentrations in the surface seawater were 2.465 mBq/m3 and 2.205 mBq/m3 for the SCS and the eastern IND seawater, respectively, and the result is consistent with the previous measurements. Then, the 239+240Pu stored in the study area of SCS and eastern IND was estimated to be approximately 1.0-1.4% of the global ocean based on the model. This study provides a useful model for guiding and designing future monitoring of pollution by anthropogenic Pu and other isotopes.

Approach for estimating the vertical distribution of the diffuse attenuation coefficient in the South China Sea

The vertical distribution of the diffuse attenuation coefficient K(z, λ) is critical for studies in bio-optics, ocean color remote sensing, underwater photovoltaic power, etc. It is a key apparent optical property (AOP) and is sensitive to the volume scattering function β(ψ, z, λ). Here, using three machine learning algorithms (MLAs) (categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF)), we developed a new approach for estimating the vertical distribution of Kd(z, 650), KLu(z, 650), and Ku(z, 650) and applied it to the South China Sea (SCS). In this approach, based on in situ β(ψ, z, 650), the absorption coefficient a(z, 650), the profile depths z, and Kd(z, 650), KLu(z, 650), and Ku(z, 650) calculated by Hydrolight 6.0 (HL6.0), three machine learning models (MLMs) without or with boundary conditions for estimating Kd(z, 650), KLu(z, 650), and Ku(z, 650) were established, evaluated, compared, and applied. It was found that (1) CatBoost models have superior performance with R2 ≥ 0.92, RMSE≤ 0.021 m-1, and MAPE≤ 4.3% and most significantly agree with HL6.0 simulations; (2) there is a more satisfactory consistency between HL6.0 simulations and MLMs estimations while incorporating the boundary conditions; (3) the estimations of Kd(z, 650), KLu(z, 650), and Ku(z, 650) derived from CatBoost models with and without boundary conditions have a good agreement with R2 ≥0.992, RMSE ≤0.007 m-1, and MAPE≤0.8%, respectively; (4) there is an overall decreasing trend with increasing depth and increasing offshore distance of Kd(z, 650), KLu(z, 650), and Ku(z, 650) in the SCS. The MLMs for estimating K(z, λ) could provide more accurate information for the study of underwater light field distribution, water quality assessment and the validation of remote sensing data products.

Observational Analysis of the Formation Reasons and Evolution Law of Winter Counter-Wind Current in Jiazi Sea Area of Northeastern South China Sea

Based on the observational data of wind, current, and sea surface temperature in the Jiazi sea area of the northeastern South China Sea in 2018 and the satellite remote sensing data of sea surface temperature in the northern South China Sea, this paper explores the formation reasons and evolution law of winter counter-wind currents in the Jiazi sea area of the northeastern South China Sea. The results show that: (1) The counter-wind current in the Jiazi sea occurs only in certain time periods instead of the entire winter; (2) When the eastern component of wind stress weakens, the eastern component of seafloor friction also weakens to some extent. A high-frequency northeast current often occurs in the bottom layer of the sea area, indicating that the formation of winter counter-wind current in the Jiazi sea area is a result of the concerted action of wind stress, the baroclinic effect, and geostrophic effect; (3) When the counter-wind current is formed, there is a low temperature water mass in the northwest of Jiazi and a high-temperature water mass in the southeast. The baroclinic effect causes the sea water to flow to the shore and produce a westward flow on the shore, and northeastward counter-wind current occurs on convergent sea water on the shore due to the baroclinic effect and geostrophic effect (Ekman effect). Therefore, two different current systems are formed in the northeastern South China Sea in winter with 116° E as the boundary. The appearance of cold water masses in the northwest of 116° E sea area and warm water masses in the southeast of the South China Sea is the key to the formation of both the two different current systems with 116° E as the boundary and the winter counter-wind flow; (4) The formation and disappearance of the counter-wind current can be divided into four stages: in the first stage, the northeast monsoon gradually relaxes to become the southeast wind, forming the northwest current; in the second stage, the warm water masses on the west side of the Luzon Strait flow to the coastal waters due to the northwest current, forming a significant onshore pressure gradient force; in the third stage, a high-temperature seawater convergence zone is formed in the Jiazi sea area, forming southwest and northeast pressure gradient forces, and the northwest coastal current forms a counter-wind current under the combined action of pressure gradient force and geostrophic effect; in the fourth stage, the northeast monsoon intensifies and the counter-wind current weakens gradually until it disappears, and the sea water flows to the southwest again.

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.

A Combination of Spatial Domain Filters to Detect Surface Ocean Current from Multi-Sensor Remote Sensing Data

This research investigates the applicability of combining spatial filter’s algorithm to extract surface ocean current. Accordingly, the raster filters were tested on 80–13,505 daily images to detect Kuroshio Current (KC) on weekly, seasonal, and climatological scales. The selected raster filters are convolution, Laplacian, north gradient, sharpening, min/max, histogram equalization, standard deviation, and natural break. In addition, conventional data set of sea surface currents, sea surface temperature (SST), sea surface height (SSH), and non-conventional data such as total heat flux, surface density (SSD), and salinity (SSS) were employed. Moreover, controversial data on ocean color are included because very few studies revealed that chlorophyll-α is a proxy to SST in the summer to extract KC. Interestingly, the performance of filters is uniform and thriving for seasonal and on a climatological scale only by combining the algorithms. In contrast, the typical scenario of identifying Kuroshio signatures using an individual filter and by designating a value spectrum is inapplicable for specific seasons and data set. Furthermore, the KC’s centerlines computed from SST, SSH, total heat flux, SSS, SSD, and chlorophyll-α correlate with sea surface currents. Deviations are observed in the various segments of Kuroshio’s centerline extracted from heat flux, chlorophyll-α, and SSS flowing across Tokara Strait from northeast Taiwan to the south of Japan.

Seasonality of primary productivity affects coastal species more than its magnitude

While the importance of extreme conditions is recognised, patterns in species' abundances are often interpreted through average environmental conditions within their distributional range. For marine species with pelagic larvae, temperature and phytoplankton concentration are key variables. Along the south coast of South Africa, conspicuous spatial patterns in recruitment rates and the abundances of different mussel species exist, with focal areas characterized by large populations. We studied 15 years of sea surface temperature (SST) and chlorophyll-a (chl-a) satellite data, using spectral analyses to partition their temporal variability over ecologically relevant time periods, including seasonal (101 to 365 days) and intra-seasonal cycles (20 to 100 days). Adult cover and mussel recruitment were measured at 10 sites along the south coast and regression models showed that about 70% of the variability in recruitment and adult cover was explained by seasonal variability in chl-a, while mean annual chl-a and SST only explained 30% of the recruitment, with no significant effect for adult cover. SST and chl-a at two upwelling centres showed less predictable seasonal cycles during the second half of the study period with a significant cooling trend during austral autumn, coinciding with one of the mussel reproductive peaks. This likely reflects recent changes in the Agulhas Current, the world's largest western boundary current, which affects coastal ecosystems by driving upwelling. Similar mechanisms probably operate in other marine systems with the potential to affect the distribution patterns of key ecosystem engineers. We propose that variability in the characteristic timescales of environmental fluctuations can explain the spatial patterns of abundance of foundational species by affecting larval recruitment. This is especially important in a context of global and pervasive climate change, as shifts in the periodicity of environmental fluctuations appear to reflect large scale climatic teleconnections driven by anthropogenic forcing.

Impacts of Seasonal Variation on Organochlorine Pesticides in the East China Sea and Northern South China Sea

To investigate the characteristics of historic-use organochlorine pesticides (OCPs) in the marginal seawater of China, we examined the seasonal and spatial distributions of hexachlorobenzene (HCB), hexachlorocyclohexanes (HCHs), and dichlorodiphenyltrichloroethane (DDTs) in the northern South China Sea (NSCS, 18-23° N) and East China Sea (ECS, 26-32° N). Seasonally, in the NSCS, the significantly higher concentrations (p < 0.05) of HCB, HCHs, and DDTs were found in summer, autumn, and summer through autumn, respectively. In the ECS, the higher concentrations were found in summer through winter, autumn, and summer. Spatially, HCB concentrations were significantly higher in the NSCS than in the ECS during all seasons except winter. During all four seasons, concentrations of HCHs were significantly higher in the NSCS than in the ECS. In summer and autumn, concentrations of DDTs were significantly higher in the NSCS than in the ECS, while no significant differences were found in spring and winter. Generally, regional usage, river-influenced coastal plumes, phytoplankton abundances, and ocean currents played crucial roles in the input, transport, degradation, and dilution of OCPs. These dynamic factors along with the seasonally alternating monsoon directly influenced the seasonal and spatial characteristics of OCPs. Furthermore, the profiles and diagnostic ratios of HCHs and DDTs revealed highly weathered OCP residues, attributed to eroded soils carried by surface runoff and long-range oceanic and atmospheric transport.

Transport of terrigenous polycyclic aromatic hydrocarbons affected by the coastal upwelling in the northwestern coast of South China Sea

Coastal upwelling prevails in the coast of Hainan Island, the northern South China Sea (SCS) during summer. We studied the influences of the upwelling on the horizontal and vertical transport of terrigenous polycyclic aromatic hydrocarbons (PAHs). PAHs in dissolved and suspended particulate phase of water samples were determined in the upper (depth < 1 m) and water column (depth > 10 m). PAH levels decreased sharply from inshore to offshore to open sea. The results showed that terrestrial input was the main source of coastal PAHs. Perylene, an important indicator of land plant-derived PAH, showed the significant correlation with PAHs (p < 0.005). This implied that fluvial transport was the primary pathway of terrigenous PAHs into the coast of northern SCS. Variations of the concentrations, compositions and diagnostic ratios of PAHs, accompanied the partition equilibrium in the water column, could indicate the selective degradation of PAHs by the plankton affected by upwelling. Different from the "traditional" transport pathway of PAHs in the water column (surface enrichment-depth depletion distribution), the upwelling could provide the original driver to elevate the upward diffusion of sediment entrained contaminants towards the intermediate even the upper waters. It could also enhance the outward diffusion of terrigenous PAHs accompanied by the offshore transport of the upper waters. Therefore, the transport pathway of PAHs can be summarized by the coastal upwelling rising PAHs with their subsequent transport offshore and settling in the adjacent open sea.