Marine Environmental Monitoring Research Articles

Harmful Algal Blooms on the Portuguese coast: Cross-checking events with remote sensing ocean colour data for coastal management

Phytoplankton plays a vital role in marine ecosystems, serving as a primary food source for a diverse array of organisms, including commercially important bivalves, crustaceans, and fish. However, in some situations, the proliferation of algae can cause serious economic losses for aquaculture, fishing, and tourism and have major environmental and human health impacts. Increases in the occurrence of toxic phytoplankton blooms in Iberian waters have been reported. Earth Observation (EO) can provide important information about the spatial and temporal distribution and the destination of blooms, with scales not available for conventional monitoring techniques. The present work aims to take advantage of public databases (Harmful Algae Event Database (HAEDAT)) to relate ocean colour data, also publicly available, (Copernicus Marine Environmental Monitoring Service (CMEMS)) with the detection of the occurrence of harmful algal blooms (HABs). From the analysis of the HAEDAT database, it was possible to observe that in Portugal, from 1987 to 2020, there were 669 HABs events, with Diarrhetic Intoxication by Shellfish (DSP) syndrome. The images were extracted from multiple ocean colour sensors (2017–2020) for periods coinciding with DSP HABs events in recent years to identify patterns in the development of HAB toxins and to assist in coastal management. Having in account the HAB events analysed (12), was observed a weak but significant correlation between the datasets when the comparison was between the biotoxin concentration and its 14th day precedent Chlorophyll a (Chl a) concentration (rSpearman=0.15). These results show how EO data relate to regular bivalve toxin monitoring programs on the Portuguese coast and are useful for future integration in modelling tools. Other than being useful for coastal water quality assessment and management, these results also reinforce the importance of maintaining existing monitoring programs on a regular basis, especially as some types of HABs may not be easily distinguishable by EO.

Risk Factors of Mariculture Supply Chain Based on Green Development: A Case Study of Changhai County in China

China is a global leader in aquaculture, and mariculture has become an important part of Chinese agriculture and one of the important growth points of the rural economy. However, environmental changes and industrial restructuring have brought new risks to the aquaculture supply chain. Recently, people have also paid more and more attention to the marine environment because a good marine environment is the foundation of the healthy development of mariculture. This study analyses the risks in the aquaculture supply chain from the perspective of the marine environment, using Changhai County, Liaoning Province, as an example. The study identified marine environmental issues such as over-sized aquaculture, irrational aquaculture structure, resource integration gaps, nuclear wastewater discharge and offshore oil pollution. Recommendations included restructuring aquaculture, professional training, establishing cooperative mechanisms, and improving seafood quality testing and marine environment monitoring. Addressing these challenges is essential to ensure the stable and healthy development of seafood markets and the sustainability of the aquaculture industry.

Dual-mode electrochemiluminescence sensing and phone imaging assays based on bipolar electrode for kanamycin detection

Excessive use of antibiotics will enter the water environment and soil through the biological chain, and then transfer to the human body through food, resulting in drug resistance, kidney toxicity and other health problems, so it is urgent to develop highly sensitive detection methods of antibiotics. Here, we designed a dual-mode sensor platform based on closed bipolar electrode (cBPE) electroluminescence (ECL) and mobile phone imaging to detect kanamycin in seawater. The prepared CN-NV-550 displayed extremely intense ECL signal, allowing for convenient mobile phone imaging. The cBPE was combined with DNA cycle amplification technology to prevent the mutual interference between target and the luminescent material, and realized the amplification of signal. In the presence of target Kana, Co3O4 was introduced to the cBPE anode by DNA cycle amplification product, and accelerated the oxidation rate of uric acid (UA). Thus, the electroluminescence response of CN-NV-550 on cBPE cathode was much improved due to the charge balance of the cBPE, achieving both ECL detection and mobile phone imaging assay of Kana, which much improved the accuracy and efficiency of assay. The limit of detection (LOD) in this work is 0.23 pM, and LOD for mobile phone imaging is 0.39 pM. This study integrate ECL imaging visualization of CN-NV-550 and high electrocatalytic activity of Co3O4 into cBPE-ECL detection, providing a new perspective for antibiotic analysis, and has great potential for practical applications, especially in Marine environmental pollution monitoring.

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Electro-mechano transduction of stacked triboelectric nanogenerator considering wave force: Modeling, validation and applications

Aiming at the low-frequency characteristics of ocean wave energy, triboelectric nanogenerators (TENGs) provide an efficient collection method. We propose a multi-layer contact-separation mode triboelectric nanogenerator (MS-TENG) based on a frequency-up mechanism to collect water wave energy. First, we study the effects of frequency rising and TENG unit stacking on the output of the MS-TENG. Experiments show that the short-circuit current of the MS-TENG with 12 layers stacked is 152.16 μA when the rotating speed is 60 rpm. Compared with a single TENG unit, the short-circuit current of the MS-TENG is increased by 6.35 times. Second, the hydrodynamic simulation method is used to study the force and energy capture efficiency of floating bodies with different shapes in water waves. The results show that the energy capture efficiency of the cylindrical floating body is 70.14 % higher than that of the spherical floating body under the same volume. Third, we use a cylindrical floating body to package the MS-TENG. In water tank experiments, when the load resistance is 4 MΩ, the peak power density of the packaged MS-TENG is 14.56 W/m3. In addition, with the help of the MS-TENG, we set up a wireless temperature and humidity monitoring system to realize the preliminary marine environmental monitoring.

Structural Design and Optimization of Low-Frequency High-Power Electromagnetic Transducer

Crucial equipment for marine engineering and research, high-power ultra-low frequency sound sources have become necessary with the rapid development of seabed resource exploration, marine environment monitoring, underwater remote target detection, and communication technology. The current ultra-low frequency sound source cannot meet the requirements of practical applications. In this paper, both the finite element method and equivalent circuit method are used to improve the electromagnetic acoustic transducer, focusing on solving the structural design of the functional parts of the transducer, as well as the mechanical, electrical, magnetic parametrization of the transducer and other key technologies. The structural parameters and material parameters in the transducer that affect the final performance response are obtained through the optimization of the genetic algorithm. The results show that the electromagnetic acoustic transducer studied in this paper can emit low-frequency sound waves below 50~Hz, and the maximum sound pressure level reaches 138~dB, which meets the requirements of general low-frequency high-power acoustic wave transducers in practical work.

Legal challenges of regulating cross-border nuclear pollution from ship ballast water under the background of fukushima wastewater release and China’s response

The Japanese government has initiated plans to release water contaminated The international community has yet to fully recognize the accelerated spread of nuclear-contaminated water through ship ballast water along the Japanese route. This increases the risk of nuclear exposure for crew members and poses ecological and environmental contamination risks to port waters, inland seas, and rivers. The lack of international attention to the marine nuclear safety crisis caused by nuclear pollution from ballast water, along with the absence of corresponding legal prevention and control mechanisms and technical safeguards, remains a critical issue. From the perspective of international law, preventing the spread of Fukushima wastewater through ship ballast water faces numerous legal challenges, including disputes over the applicability of conventions, the jurisdiction of international organizations, and mechanisms for cross-border accountability for marine nuclear pollution. As a major shipping nation adjacent to Japan, China faces significant risks of nuclear pollution from ballast water along the Japanese route. To counter such risks, it is recommended to propose measures through the International Maritime Organization to elevate international attention. Moreover, refining ballast water regulatory frameworks, establishing monitoring and warning mechanisms specific to nuclear pollution from ballast water, improving the technical capabilities of ballast water treatment systems, and promoting international cooperation in monitoring marine environments and preventing pollution are also advisable.

Integrated assessment of trace elements in a marine ranching area based on multi-species and multi-level biomarkers: a case study in China’s national-level marine ranching demonstration area

The goal of this study was to evaluate the trace element pollution in a marine ranching area in China based on molecular responses (expression of metallothionein and antioxidant enzyme genes), and biochemical biomarkers (metallothionein content, antioxidant enzyme activities, and malonaldehyde level) in four indicator species. We collected samples of two fish (Lateolabrax maculatus and Sebastes schlegelii), one crustacean (Charybdis japonica), and one gastropod (Rapana venosa) from the western Furong Island marine ranching area and from an adjacent area in March 2022 and measured the trace element content in these indicator species as well as in the seawater and sediment. We found that the bioaccumulation characteristics of trace elements and the response patterns of biomarkers were species specific. Moreover, not every biomarker was significantly correlated with environmental trace element content. We then established two biomarker combinations indicative of trace element pollution in seawater and sediment, respectively, based on the correlation between biomarkers and trace element contents. The selected biomarkers were integrated using integrated biomarker response version 2 (IBRv2). IBRv2 values in the studied marine ranching area were lower than those in the adjacent area. Additionally, these values were consistent with the bioaccumulation of trace elements in the indicator species, the integrated trace element pollution index for seawater, and the potential risk index for sediment. These results show that this multi-biomarker and multi-species IBRv2 approach provided a comprehensive diagnosis of trace element pollution in the marine ranching area. Therefore, its application may be beneficial for marine environmental monitoring and management in view of the ecotoxicological impact of pollutants on organisms.

Long-term carbaryl exposure leads to behavioral abnormalities and reproductive toxicity in male marine medaka through apoptosis-mediated HPA and HPG axes dysregulation

Carbaryl is a widely used carbamate pesticide that has been detected in the marine environment, but its effects on marine fish are still unknown. This study was aimed to investigate the effects of long-term exposure of carbaryl on male marine medaka. For this purpose, we set up five exposure concentration groups of 0, 0.1, 1, 10, and 100 µg/L for 180 days. On the one hand, we observed increased aggression and decreased ability to avoid predators in males after exposure, which was affected by the levels of HPA-axis hormones, especially decreased cortisol level. On the other hand, after exposure, HPG axis hormone levels and gene transcription levels were disturbed. Males exhibited a decreased gonadosomatic index and a notable reduction in mature sperm proportion and the F1 generation displayed a significant increase in malformation rate. Additionally, the number of apoptotic cells and the transcription level of apoptosis-related genes in the brains of male marine medaka substantially increased after exposure. Apoptosis of brain cells may be responsible for the disturbance of HPA and HPG axes, consequently leading to behavioral and reproductive abnormalities. These findings provide novel insights into evaluating the toxic effects of carbaryl on male marine medaka and emphasizing the criticality of exploring the potential environmental risks posed by carbaryl in the marine environment, thus providing toxicity value basis for further strengthening marine environmental monitoring and the protection of biological resources.

A self-powered smart wave energy converter for sustainable sea

Self-powered smart buoys are widely used in sustainable sea, such as marine environmental monitoring. The article designs a self-powered and self-sensing point-absorber wave energy converter based on the two-arm mechanism. The system consists of the wave energy capture module, the power take-off module, the generator module and the energy storage module. As the core component of the wave energy converter, the power take-off module is mainly composed of a two-arm mechanism, which can convert the oscillation heave motion into unidirectional rotary motion. To evaluate the power generation performance of the system, the kinematic and dynamic models of the wave energy converter with the flywheel are established, and the disengagement and engagement phenomena of the flywheel are analyzed. The effectiveness of the prototype in capturing wave energy is verified through dry experiments in lab and field tests. The dry experiment reveals that the maximum output power of the system is 5.67 W, and the maximum and average mechanical efficiency are 66.63 % and 48.35 %, respectively. Additionally, the field test demonstrates that the peak output power can reach 92 W. Meanwhile, the generated electrical signals can be processed by deep learning algorithms to accurately identify different wave states. This high performance confirms that the proposed wave energy converter can meet its own energy needs by capturing wave energy in the marine environment, while also achieving self-sensing for wave condition monitoring. The system has great potential for promoting the development of intelligent sustainable sea in the future.

Underwater Fish Object Detection with Degraded Prior Knowledge

Understanding fish distribution, behavior, and abundance is crucial for marine ecological research, fishery management, and environmental monitoring. However, the distinctive features of the underwater environment, including low visibility, light attenuation, water turbidity, and strong currents, significantly impact the quality of data gathered by underwater imaging systems, posing considerable challenges in accurately detecting fish objects. To address this challenge, our study proposes an innovative fish detection network based on prior knowledge of image degradation. In our research process, we first delved into the intrinsic relationship between visual image quality restoration and detection outcomes, elucidating the obstacles the underwater environment poses to object detection. Subsequently, we constructed a dataset optimized for object detection using image quality evaluation metrics. Building upon this foundation, we designed a fish object detection network that integrates a prompt-based degradation feature learning module and a two-stage training scheme, effectively incorporating prior knowledge of image degradation. To validate the efficacy of our approach, we develop a multi-scene Underwater Fish image Dataset (UFD2022). The experimental results demonstrate significant improvements of 2.4% and 2.5%, respectively, in the mAP index compared to the baseline methods ResNet50 and ResNetXT101. This outcome robustly confirms the effectiveness and superiority of our process in addressing the challenge of fish object detection in underwater environments.

The operational CMEMS wind wave forecasting system of the Black Sea

ABSTRACT Accurate wind wave data is essential for ship navigation, safety at sea, and offshore energy production. Access to real-time and forecast wave data, with quality metrics, is vital for operational services. The Copernicus Marine Environment Monitoring Service (CMEMS) operational wave forecasting system of the Black Sea relies on the third-generation WAve Model (WAM). It offers a 1-day hindcast and 10-day forecast at 2.5 km spatial and 1-hour temporal resolutions. The model is driven by ECMWF IFS010 winds as well as surface currents and sea surface heights from a hydrodynamic model. The model's validation against satellite and buoy data indicates good overall performance. For two-year satellite significant wave heights, error statistics include a root mean square difference (RMSD) of 27 cm, a bias of – 16 cm, a scatter index of 0.22, and a correlation coefficient of 0.93. Wave buoy comparisons show a RMSD of 1.09 s for the TM02 period and a 30.5° standard deviation of the mean direction. The model performs well during storm events and provides reliable wave forecasts for up to four days, despite slightly underestimating the significant wave height. The driving wind fields generally perform well, but assessments reveal shortcomings along the coasts.

Elastic Self-Recovering Hybrid Nanogenerator for Water Wave Energy Harvesting and Marine Environmental Monitoring.

To achieve large-scale development of triboelectric nanogenerators (TENGs) for water wave energy harvesting and powering the colossal sensors widely distributed in the ocean, facile and scalable TENGs with high output are urgently required. Here, an elastic self-recovering hybrid nanogenerator (ES-HNG) is proposed for water wave energy harvesting and marine environmental monitoring. The elastic skeletal support of the ES-HNG is manufactured using three-dimensional (3D) printing technology, which is more conducive to the large-scale integration of the ES-HNG. Moreover, the combination of a TENG and an electromagnetic generator (EMG) optimizes the utilization of device space, leading to enhanced energy harvesting efficiency. Experimental results demonstrate that the TENG achieves a peak power output of 42.68 mW, and the EMG reaches a peak power output of 4.40 mW. Furthermore, various marine environment monitoring sensors, such as a self-powered wireless meteorological monitoring system, a wireless alarm system, and a water quality monitoring pen, have been successfully powered by the sophisticated ES-HNG. This work introduces an ES-HNG for water wave energy harvesting, which demonstrates potential in marine environment monitoring and offers a new solution for the sustainable development of the marine internet of things.

Real-time underwater object detection technology for complex underwater environments based on deep learning

Underwater object detection technology is crucial in many marine-related fields, including marine environmental monitoring, marine resource development, and marine ecological protection. However, this technology faces great challenges due to the poor quality of underwater optical images and the varying sizes of underwater objects. Therefore, we proposed an underwater optical detection network (UODN) based on the you only look once version 8 (YOLOv8) framework, which addresses these issues through the cross stage multi-branch (CSMB) module and large kernel spatial pyramid (LKSP) module. The aim of the CSMB module is to extract more features from underwater optical images to address the issue of poor image quality, while the LKSP module is designed to enhance the ability of the network to detect underwater objects of various scales. Furthermore, CSMBDarknet built by CSMB and LKSP can be used as the backbone of other underwater object detection algorithms for underwater feature extraction. Extensive experimental results on the underwater robot professional contest 2020 dataset revealed that the average precision (AP) of UODN increased by 1.0%, the AP50 of UODN increased by 1.1%, and the AP75 of UODN increased by 2.1% compared with those of the original YOLOv8s. Furthermore, UODN outperforms 12 state-of-the-art models on multiple underwater optical datasets, paving the way for future real-time and high-precision underwater object detection.

Variability of Indonesian Throughflow Transport in the Indonesian Seas during Triple-Dip La Niña

Abstract The Indonesian seas serve as an important conduit for the Indonesian Throughflow (ITF), a critical system of warm water and freshwater transport connecting the Pacific and Indian Oceans. El Niño-Southern Oscillation (ENSO) significantly impacts the regional variability in the Pacific and exerts a profound influence on water mass transport from the Pacific to the Indian Oceans. This study focuses on the influence of triple-dip La Niña events during two distinct periods (1998 – 2001 and 2020 – 2023) on the variability of ITF transport in key areas of the Indonesian seas, including the Makassar, Lombok, and Ombai Straits, Flores and Banda Seas, and Timor Passage. Using forecast and reanalysis data from the Copernicus Marine Environment Monitoring Service (CMEMS), transport calculations are conducted across cross-sectional profiles representing the study areas. The results reveal that the volume transport is significantly higher at depths ranging from ∼50 – 300 m. It also shows seasonal fluctuations in volume transport across all regions, with lower transport during winter (DJF) and higher transport during summer (JJA) and transition period (SON) in the Makassar and Lombok Straits. In the Flores and Banda Seas, Ombai Strait, and Timor Passage, the surface volume transport follows seasonal patterns, while at deeper depths (∼50 – 300 m and 300 – 800 m) exhibits distinct fluctuations compared to the surface. Triple-dip La Niña events lead to an overall increase in volume transport during both periods, with the 2019 – 2023 period showing larger values (∼19 Sv) compared to the 1997 – 2001 period (∼14 Sv). The strengthened ocean current during triple-dip La Niña events results in more significant variations in total transport, leading to larger differences between minimum and maximum transport during these periods. The volume transport in Makassar Strait is further analyzed using the Empirical Orthogonal Function (EOF) that identifies three dominant modes (86.4%), revealing the influence of semi-annual, interannual, and decadal factors on the observed variability.

Blind source separation algorithm for noisy hydroacoustic signals based on decoupled convolutional neural networks

Wireless communication technology has been widely used in marine engineering, marine ranching and Marine environmental monitoring. However, structural redundancy and functional confusion exist in applying neural networks in signal separation technology in underwater communication environments, which can result in a slower rate of signal separation and lead to confusion of parameters during transfer learning. Based on this, an end-to-end, internal functionally structured decoupled neural network (D-CNN) blind source separation (BSS) model is proposed in this paper, which can realize a neural network BSS algorithm with a well-defined structure and function. The one-dimensional convolutional neural network layer is used in algorithm to automatically extract observed signal's features, based on the features, and there are two generation modules of separation matrix and scaling coefficients. Then the two modules can be used to separate the observed signal and adjust the signal coefficients to obtain the separated signal. Finally the transfer learning technique is used to generalize the model, which reduces the transfer cost of the model in different application scenarios. Experimental results show that when the communication distance is set to 0.02 km–2 km, the MSE of independent signal and related signal can be reduced by 14.24% and 14.95% respectively compared with the nearest Neural FCA algorithm. The results prove that the proposed algorithm can accurately estimate the source signal and improve the signal reception quality.

Wave retrieval for Sentinel-1 synthetic aperture radar under complex sea state

ABSTRACT In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m.

Bidirectional Rotating Turbine Hybrid Triboelectric‐Electromagnetic Wave Energy Harvester for Marine Environment Monitoring

AbstractHydrokinetic energy, a vital renewable resource, holds promise for addressing fossil fuel shortages to ensure a sustainable future. This study proposes an efficient and stable ocean wave energy harvesting system that combines a triboelectric nanogenerator (TENG), an electromagnetic generator (EMG) with a planetary gear system, and a driving turbine. The turbine transforms the up‐and‐down motion in water into continuous, unidirectional rotational motion. An EMG device accelerated by a planetary gear system further ensures sustained output at very low frequencies (0.25 Hz) and boosts the upper limit of power generation at medium to high frequencies. Under water wave conditions with a 1 Hz frequency, the TENG component yields an output of up to 2200 V and 122 µA, while the average EMG output is 15 V and 80 mA. Remarkably, the entire system maintains a steady and uninterrupted output even at extremely low frequencies (0.25 Hz). The peak power outputs for the TENG and EMG components are 115 and 350 mW, respectively, with power densities of 32.55 and 329.78 W m−3, surpassing prior achievements. This research demonstrates self‐powered applications and provides an efficient method to amplify water wave forces, enhancing energy harvesting capabilities for practical marine environmental monitoring.

A broadband hybrid blue energy nanogenerator for smart ocean IoT network

Blue energy represents a crucial renewable energy resource; however, its development has been limited by the low and variable frequency characteristics of sea waves. In this study, we present a novel approach, wherein a spring pendulum coupled hybrid energy harvester (SPC-HEH) is employed to address these limitations. The SPC-HEH integrates two electromagnetic generators (EMGs), two piezoelectric nanogenerators (PENGs), and two multilayer-structured triboelectric nanogenerators (TENGs) into a vessel, enabling efficient wave energy harvesting. Notably, the integration of the lightweight PENG and TENG with the heavy EMG not only enhances the water wave energy harvesting capability of SPC-HEH but also optimizes its spatial utilization efficiency. Additionally, owing to the spring pendulum, the SPC-HEH is capable of simultaneously harnessing the wave kinetic energy of horizontal and vertical directions and realize the decoupling of vertical energy capture from horizontal energy capture. Remarkably, owing to its precise geometric design and efficient space utilization, the SPC-HEH achieves an impressive peak power density of 82.4 Wm−3. Finally, we demonstrate the practical applicability of the SPC-HEH by integrating it with a circuit as a self-powered ocean buoy for real-time monitoring of ocean temperature, wave frequency, ocean turbidity and other information. The remarkable potential of this system for integration into self-powered smart ocean IoT networks holds significant promise for advancing marine research and environmental monitoring capabilities.

Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms

Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.