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Seawater electrolysis represents a promising green energy technology with significant potential for efficient energy conversion. This study provides an in-depth examination of the key scientific challenges inherent in the seawater-electrolysis process and their potential solutions. Initially, it analyzes the potential issues of precipitation and aggregation at the cathode during hydrogen evolution, proposing strategies such as self-cleaning cathodes and precipitate removal to ensure cathode stability in seawater electrolysis. Subsequently, it addresses the corrosion challenges faced by anode catalysts in seawater, introducing several anti-corrosion strategies to enhance anode stability, including substrate treatments such as sulfidation, phosphidation, selenidation, and LDH (layered double hydroxide) anion intercalation. Additionally, this study explores the role of regulating the electrode surface microenvironment and forming unique coordination environments for active atoms to enhance seawater electrolysis performance. Regulating the surface microenvironment provides a novel approach to mitigating seawater corrosion. Contrary to the traditional understanding that chloride ions accelerate anode corrosion, certain catalysts benefit from the unique coordination environment of chloride ions on the catalyst surface, potentially enhancing oxygen evolution reaction (OER) performance. Lastly, this study presents the latest advancements in the industrialization of seawater electrolysis, including the in situ electrolysis of undiluted seawater and the implementation of three-chamber dual anion membranes coupled with circulating electrolyte systems. The prospects of seawater electrolysis are also explored. [ABSTRACT FROM AUTHOR]

An attention U-Net was proposed to reconstruct the missing chlorophyll-a concentration (Cchla) data. The U-Net is a lightweight full convolution neural network architecture consisting of an enccoder-decoder (i.e., down-sampling and up-sampling). The attention gates (AGs) were integrated into the U-Net. Training the U-Net with AGs could implicitly teach it to suppress irrelevant areas and highlight the salient features in the missing data areas, which would increase the network sensitivity and reconstruction accuracy. The neural network uses the satellite-derived Cchla anomalies and its variance as the input, and the reconstructed fields along with their variances as outputs. The trained network was applied to long-term daily MODIS/Aqua Cchla products in the Pearl River estuary (PRE) and adjacent continental shelf area. The model performance was evaluated by using an independent test dataset from both satellite-derived and in-situ measurements. The results showed that the proposed neural network not only had good performance in the reconstruction of valid pixels, but also provided a more reasonable reconstruction compared to the standard U-Net without AGs. This study provided a feasible method for the reconstruction task in the field of ocean color, which should be helpful in producing a creditable dataset to study the ecological effects of extreme weather conditions such as typhoons on the upper ocean in the PRE waters. Based on the reconstructed Cchla products, the footprints of the typhoons were studied. An increase in surface Cchla near the typhoons' track and a decrease in estuary were found. The composite results illustrated that the Cchla increases occurred for almost the entire area within a radius of 100 km. The time series analysis showed that the Cchla peak appeared on the fifth day after the typhoon's passage. [ABSTRACT FROM AUTHOR]

Phytoplankton dynamics, which are highly sensitive to the ecosystem condition and change, are different in coastal waters and open ocean. Previous researches mainly focused on the open ocean dynamic in the South China Sea (SCS), but few research on the Eastern Guangdong (EGD), especially during a period of downwelling-favorable winds. In fact, the influence of topographic irregularities, downwelling, continental outflows, and cold, nutrient-rich currents from Fujian-Zhejiang coastal waters (ZFC) of the East China Sea (ECS) on the spatial distribution of Chlorophyll a (Chl a) in EGD coastal waters could be significant. This study utilized ocean color time series and meteorological and hydrographic data jointly from July 2002 to June 2020 to investigate the heterogeneous regional specific distribution pattern of Chl a in EGD and the environmental determinants in different regions subdivided by water depth. Then the temporal and spatial distribution pattern of Chl a in EGD in winter were discussed in detail by applying an Empirical Orthogonal Function (EOF) analysis, GaoFeng-1 (GF-1) satellite data and in situ measured dataset. The sea surface temperature (SST) in February was negatively related with Chl a in shallow coastal waters less than 60 m deep due to the nutrient-rich, cold waters from ZFC. The monthly mean photosynthetically active radiation (PAR) and precipitation showed significant effects on the phytoplankton growth over regions with a depth less than 10 m. An area with higher Chl a concentration in the downwelling zone were detected in winter. By an examining the temporal variability in meridional distribution of the mean Chl a at 22.41°N and 22.21°N, a symmetrical peak was observed. The coastal fronts extended southwestward from the southeastern coast of Guangdong Province to Dangan Island in the SCS. In addition, a cross-shelf filament was detected near the coast of Shanwei, Guangdong, China on 8 January 2020. [ABSTRACT FROM AUTHOR]

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua's visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters. [ABSTRACT FROM AUTHOR]

The Hong Kong-Zhuhai-Macau Bridge crosses the Pearl River Estuary and is the largest bridge and tunnel project in the world. During the construction period of this project, the excessive suspended sediment was found in the construction region. The suspended sediment generated by sand excavation in the upstream was assumed to have a significant impact on the suspended sediment in the tunnel region. In this study, we assessed the impact of upstream sand excavation on the suspended sediment in the Hong Kong-Zhuhai-Macau Bridge construction area using Landsat OLI, ETM+, and TM data. Regional suspended sediment algorithms were developed for Landsat using a symbolic regression method based on data from in situ measurements in the study area from 2003 to 2014. A band shift was conducted on the remote sensing reflectance data from Landsat ETM+ and OLI to produce a time series of the suspended sediment concentrations that was internally consistent with that of the Landsat TM data. The suspended sediment distribution was extracted and used to compare under two different conditions, with and without sand excavation. The correlations of the time series of the suspended sediment concentrations in different regions in the surrounding waters, including the correlations between the construction regions and the sand excavation regions, were calculated. Our results indicated that the sand excavation north of the Pearl River Estuary had a limited impact on the surface suspended sediment concentrations in the Hong Kong-Zhuhai-Macau Bridge tunnel area. [ABSTRACT FROM AUTHOR]

We evaluated six empirical and semianalytical models of the diffuse attenuation coefficient at 490 nm (Kd(490)) using an in situ dataset collected in the Pearl River estuary (PRE). A combined model with the most accurate performance (correlation coefficient, R2 = 0.92) was selected and applied for long-term estimation from 2003 to 2017. Physical and biological processes in the PRE over the 14-year period were investigated by applying satellite observations (MODIS/Aqua data) and season-reliant empirical orthogonal function analysis (S-EOF). In winter, the average Kd(490) was significantly higher than in the other three seasons. A slight increasing trend was observed in spring and summer, whereas a decreasing trend was observed in winter. In summer, a tongue with a relatively high Kd(490) was found in southeastern Lingdingyang Bay. In Eastern Guangdong province (GDP), the relatively higher Kd(490) value was found in autumn and winter. Based on the second mode of S-EOF, we found that the higher values in the eastern GDP extended westward and formed a distinguishable tongue in winter. The grey relational analysis revealed that chlorophyll-a concentration (Cchla) and total suspended sediment concentration (Ctsm) were two dominant contributors determining the magnitude of Kd(490) values. The Ctsm-dominated waters were generally located in coastal and estuarine turbid waters; the Cchla-dominated waters were observed in open clear ocean. The distribution of constituents-dominated area was different in the four seasons, which was affected by physical forces, including wind field, river runoff, and sea surface temperature. [ABSTRACT FROM AUTHOR]