With the construction and deployment of seafloor observatories around the world, massive amounts of oceanographic measurement data were gathered and transmitted to data centers. The increase in the amount of observed data not only provides support for marine scientific research but also raises the requirements for data quality control, as scientists must ensure that their research outcomes come from high-quality data. In this paper, we first analyzed and defined data quality problems occurring in the East China Sea Seafloor Observatory System (ECSSOS). We then proposed a method to detect and repair the data quality problems of seafloor observatories. Incorporating data statistics and expert knowledge from domain specialists, the proposed method consists of three parts: a general pretest to preprocess data and provide a router for further processing, data outlier detection methods to label suspect data points, and a data interpolation method to fill up missing and suspect data. The autoregressive integrated moving average (ARIMA) model was improved and applied to seafloor observatory data quality control by using a sliding window and cleaning the input modeling data. Furthermore, a quality control flag system was also proposed and applied to describe data quality control results and processing procedure information. The real observed data in ECSSOS were used to implement and test the proposed method. The results demonstrated that the proposed method performed effectively at detecting and repairing data quality problems for seafloor observatory data.

Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST prediction for analyzing El Niño–Southern Oscillation (ENSO). However, existing approaches for SST prediction train separate models for different temporal scales, which is inefficient and cannot take advantage of the correlations among the temperatures of different scales to improve the prediction performance. In this work, we propose a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales. MIMO is an encoder–decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales. To our best knowledge, this is the first work to predict SST at different temporal scales simultaneously with a single model. According to the experimental evaluation on the Optimum Interpolation SST (OISST) dataset, MIMO achieves the state-of-the-art prediction performance.

Oil spills are major threat to marine ecosystem and have long-lasting effect on marine life and water quality. In this study, a two-dimension hydrodynamic and oil spill transport model of the Yangtze River Estuary is established based on MIKE21 with a special attention to wind-wave-current interaction (WWCI). The model agrees well with the observed data on water level, current velocity, and the real oil spill event during Typhoon Fongwong. This study is mainly focused on the oil particle trajectory and spread. The model results show: (1) when the oil spill occurs during a typhoon period, the oil film can rapidly deposit under high WWCI, decreasing the swept area to about 20% compared with the normal weather condition; (2) strong current and large wave enlarge the oil film coverage whereas high wind speeds cause the oil particles to deposit in the shallow water area; and (3) the oil particles move farther and the swept area is far greater under the winter wind than under the summer wind, and the two times of the winter wind show the greatest effect on the oil spill. This study considers the drift, dispersion, evaporation, and emulsification of oil during the moving period under different wind, wave and current conditions, providing a good guidance for the oil spill prevention and mitigation in other estuaries.

Green tides have increasingly become a nuisance worldwide in recent years, and especially in China. Since 2015, green tides have started recurring in Jinmeng Bay, Qinhuangdao, western Bohai Sea of China, and have severely deteriorated the tourism environment there. In order to investigate the migration process of the green tides in Jinmeng Bay, a hydrodynamic model and a particle-tracking model were applied based on the latest green tide event in August 2021. The hydrodynamic model was applied with triple-level 2DH meshes with different refinements and scales, which provided the hydrodynamics to drive the green macroalgae into the particle-tracking model. From the model results, the semi-enclosed waters surrounded by multiple artificial structures are a low-energy hydrodynamic environment, which is not helpful for water exchange and thus the dispersal of nutrients. The green macroalgae are distributed substantially within the semi-enclosed waters, and few are transported out with low biomass. The effects of wind and artificial structures both increase the coverage of the green macroalgae trajectories; the effect of wind plays a more important role. A sensitivity analysis of the effect of wind showed that 6 m/s wind in ENE led to the maximum coverage of the green macroalgae trajectories in the cases of different magnitudes and directions of winds. This study can provide references for the pre-warning and mitigation of green tides in Jinmeng Bay and other similar places.

In recent years, attribute-based encryption (ABE) has been widely applied in mobile computing, cloud computing, and the Internet of things, for supporting flexible and fine-grained access control of sensitive data. In this paper, we present a novel attribute-based encryption scheme that is based on bilinear pairing over Barreto and Naehrig curves (BN-curves). The identity-based encryption scheme SM9, which is a Chinese commercial cryptographic standard and a forthcoming part of ISO/IEC11770-3, has been used as the fundamental building block, and thus we first introduce SM9 and present our SM9 implementation in details. Subsequently, we propose the design and implementation of the ABE scheme. Moreover, we also develop a hybrid ABE for achieving lower ciphertext expansion rate when the size of access structure or plaintext is large. The performance and energy consumption of the implementation of the proposed ABE and its hybrid version are evaluated with a workstation, a PC, a smart phone, and an embedded device. The experimental results indicated that our schemes work well on various computing platforms. Moreover, the proposed schemes and their implementations would benefit developers in building applications that fulfill the regulatory compliance with the Chinese commercial cryptographic standard since there is no existing ABE scheme compatible with any Chinese cryptographic standard.

Qinhuangdao, a famous coastal resort city in northeastern China, has been affected by the rapid growth of harmful algae. The brown tide induced by Aureococcus anophagefferens (A. anophagefferens) first occurred along Qinhuangdao’s coastline in 2009, and it then developed into a harmful alga for the Bohai Sea. Based on MIKE 21, we construct a 2D coupled hydrodynamic and transport model to study the variation and distribution of total nitrogen (TN), and establish the relationship between A. anophagefferens, dissolved organic nitrogen (DON), and TN to provide a fast forewarning system for brown tide in Qinhuangdao. This model considers the decay, diffusion, and settling of TN, and the model results are in good agreement with the measured tidal level, current, and TN, indicating that the model is capable of capturing the observed TN distribution during the brown tide period. The transfer function relating TN to A. anophagefferens leads to a reliable fast forewarning and monitoring system. Moreover, the transferred A. anophagefferens cell density can be used to forecast the regional risk level of brown tide with a specific color indicator. The implementation of national policy relating to marine ecosystems decreases the nearshore concentration of N, P, and other nutrients, and therefore decreases harmful algal blooms. The fast routine assessment of brown tide by the present warning system provides robust guidance for the government to take action.