Marine Aquaculture Area Research Articles

Regional distribution differences of antibiotics in tropical marine aquaculture area: Insights into antibiotic management and risk assessment

In recent years, global demand for marine aquaculture products has led to a significant rise in antibiotic use, particularly in tropical coastal aquaculture areas However, research on antibiotic residues in these environments remains limited, hindering a comprehensive understanding of their environmental presence and associated risks. This study investigates the regional distribution, ecological risks, and sources of 44 antibiotics in seawater across four coastal aquaculture areas in Hainan island (Wenchang, Sanya, Danzhou, and Wanning). Among the 44 antibiotics tested across 42 sampling sites, all were detected with a 100 % detection rate. Antibiotics such as Trimethoprim (TMP), Sulfanitran (APNPS), Sulfaquinoxaline (SQ), Sulfadimethoxine (SDT), Chloramphenicol (CHP), and Florfenicol (FLO) were consistently detected across all sampling sites. Total concentrations of detected antibiotics ranged from 0 to 818.79 ng.L−1, with sulfonamide antibiotics ranging from 0 to 629.49 ng.L−1, chloramphenicol antibiotics from 0 to 87.39 ng.L−1, tetracyclines from 0 to 221.39 ng.L−1, and fluoquinolones from 0 to 272.08 ng.L−1. The highest levels of antibiotic pollution were observed at the W5 sampling site in Wenchang, attributed to aquaculture wastewater discharge, while no antibiotics were found at D12 in Danzhou. In these regions, source analysis identified aquaculture and domestic sewage as the primary contributors to antibiotic pollution in these regions. Correlation analysis with environmental factors revealed significant influences of factors such as SAL, kPa, TN, SPC, and pH on sulfonamide and chloramphenicol antibiotics. Health risk assessment indicated moderate to high risks to aquatic organisms from antibiotics like NOR, CIP, ENR, OFL, TMP, and SMX in the study areas, underscoring the need for preventive measures, stricter regulation of antibiotic use, and enhanced ecological risk monitoring in aquaculture regions. This study provides critical insights into antibiotic contamination in Hainan's coastal aquaculture areas, highlighting the urgent need for further research into the occurrence and ecological impacts of these emerging pollutants in marine environments.

Distribution and geochemical speciation of metals in the sediments of marine aquaculture areas in Japan.

Metal contamination is a crucial environmental issue because of its persistence and toxicity, which can have deleterious effects on aquatic ecosystems. Coastal sediments serve as reservoirs for metals. Aquaculture is an important human activity in coastal areas; however, the actual status of metal distribution in these areas has not been well characterized. We investigated the distribution and geochemical speciation of metals in the sediments of the Japanese aquaculture areas of Shidugawa and Furue Bays. Total metals were generally in the order Zn > Cu > Ni > Cd > Pb and occurred at high levels at the sites in the inner parts and near the fish cages in the sampling areas. Pearson's correlations and principal component analysis results suggested that the metals in these aquaculture areas might originate from several sources, such as fish feces, fish feeds, corrosion of antifouling material residues, and local anthropogenic activities, including fertilizers and manures. Furthermore, metals were mainly in silts and fine sediments, associated with residual speciation, followed by organic fractions, oxides, and high fractions of exchangeable Cd. These results provide important insights into the accumulation of metals in sediments for better managing marine aquaculture in Japan.

Intelligent Detection of Marine Offshore Aquaculture with High-Resolution Optical Remote Sensing Images

The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models’ performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery.

Evaluation of the Pacific oyster marine aquaculture suitability in Shandong, China based on GIS and remote sensing

The Pacific oyster (Crassostrea gigas) is a marine aquaculture species with rapid production growth in recent years. China accounts for nearly 90% of global production by 2021, especially in Shandong province. Evaluating suitability is crucial for ensuring the sustainable growth of Pacific oyster marine aquaculture and achieving a blue transition. This study developed a suitability evaluation model for Pacific oyster marine aquaculture using a Geographic Information System (GIS), Maximum Entropy (MaxEnt) model, remote sensing, and reanalysis data. A literature review and Analytic Hierarchy Process (AHP) were used to establish an evaluation model encompassing water quality, hydrology, climate and meteorology, and socioeconomic factors. The results showed that within a 20 km range of the Shandong coast, 49% of the area was highly suitable, 51% was moderately suitable, and the overall annual high score proportion (HSP) fluctuated around 50%, with higher suitability observed in the spring and autumn. The inner bays of the coastal areas (Laizhou, Rongcheng, Jimo) exhibited high suitability (HSP over 80%); in contrast, the offshore areas (Changdao, Rushan) farther from the coast had lower suitability and showed significant monthly variations. The result was consistent with the spatial distribution and temporal variation of Shandong’s existing Pacific oyster marine aquaculture areas. The study also found that El Niño significantly impacts Rongcheng, Rushan, and Jimo during summer. We predicted an overall increase of suitability in the Shandong offshore areas under future climate change scenarios, with a more significant increase of suitability in the north. El Niño-Southern Oscillation (ENSO) influenced the concentration of parameters such as chlorophyll-a (Chl-a) and total suspended sediment (TSS) in the coastal waters through its impact on precipitation (Pr), resulting in suitability fluctuations.

Distribution characteristics of antibiotic resistance genes and microbial diversity in the inshore aquaculture area of Wenchang, Hainan, China

The rapid development of marine aquaculture has led to the increased use and release of antibiotics into the marine environment, consequently contributing to the emergence of antibiotic resistance. Information on antibiotic resistance in nearshore marine aquaculture areas remains limited, and research on the microbial composition and potential hosts of antibiotic resistance genes (ARGs) in marine aquaculture areas is scarce. This study used SmartChip real-time fluorescent quantitative PCR and qPCR to quantitatively analyze 44 ARGs and 10 mobile genetic elements (MGEs) genes in 12 sampling points in the nearshore aquaculture area of Wenchang. High-throughput sequencing of 16S rRNA was used to study microbial diversity in the study area, to clarify the correlation between ARGs, MGEs, and microbial diversity, and to determine the possible sources and potential hosts of ARGs. The results showed that a total of 37 ARGs and 8 MGEs were detected in the study area. The detection rate of 9 ARGs (aac(6′)-Ib(aka aacA4)-02, catA1, cmlA, cfr, sul1, sul2, sulA/folP-01, tetC, tetX) was 100 %. The absolute abundance of ARGs in the 12 sampling points ranged from 2.75 × 107 to 3.79 × 1010 copies·L−1, and the absolute abundance of MGEs was 1.30 × 105 to 2.54 × 107 copies·L−1, which was relatively high compared to other research areas. ARGs and MGEs were significantly correlated, indicating that MGEs play an important role as a mediator in the spread of ARGs. At the phylum level, Proteobacteria and Cyanobacteria were the dominant bacteria in the study area, with HIMB11 and unidentifiedChloroplast being the dominant levels, respectively. Network analysis of ARGs and microorganisms (genus level) revealed that Cognatishimia, Thalassobius, Aestuariicoccus, Thalassotalea, and Vibrio were significantly correlated with multiple ARGs and were the main potential hosts of ARGs in the nearshore waters of Wenchang.

Coastal Water Clarity in Shenzhen: Assessment of Observations from Sentinel-2

Shenzhen is a crucial city in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). With high-intensity land development and rapid population growth, the ocean has become an essential space for expansion, leading to significant variations in water quality in the coastal area of Shenzhen. Water clarity (Zsd) is a key indicator for evaluating water quality. We applied the quasi-analytical algorithm (QAA) to Sentinel-2 data and retrieved the Zsd of the coastal area of Shenzhen. By adjusting the red band for distinguishing water types, we avoided underestimating Zsd for clear water. This study pioneered the production of a 10 m Zsd product for the coastal area of Shenzhen from 2016 to 2021. The results showed that the coastal area of Shenzhen exhibited a spatial distribution pattern with low Zsd in the west and high in the east, with Pearl River Estuary (PRE: 0.41–0.67 m) and Shenzhen Bay (SZB: 0.30–0.58 m) being lower than Dapeng Bay (DPB: 2.7–2.9 m) and Daya Bay (DYB: 2.5–2.9 m). We analyzed the seasonal and interannual variations and driving factors of the four areas, where PRE and SZB showed similar variation patterns, while DPB and DYB showed similar variation patterns. PRE and SZB are important estuaries in southern China, significantly affected by anthropogenic activities. DPB and DYB are important marine aquaculture areas, mainly affected by natural factors (wind speed, precipitation, and sea level). The Zsd of the coastal area of Shenzhen, along with the analysis of its results and driving factors, contributes to promoting local water resource protection and providing a reference for formulating relevant governance policies. It also provides a practical method for assessing and monitoring near-shore water quality.

Constraining the origin of sedimentary organic matter in the eastern Guangdong coast of China using δ13C and δ15N

The source apportionment of organic carbon is of great scientific significance for understanding the carbon sink pattern in the coastal ocean. The concentrations of total organic carbon (TOC) and total nitrogen (TN) and their isotopic compositions (δ13CTOC and δ15NTN) were determined in surface sediments and sediment cores (Y3 and Y5) collected in the eastern Guangdong coast (EGDC) during spring, summer and winter cruises in 2021, in order to determine the distribution and source of sedimentary organic matter. Spatially, TOC and δ13CTOC distributions in the EGDC have contrasting patterns; TOC concentrations decreased and δ13CTOC values increased with distance offshore. Higher TOC concentrations and enriched δ13CTOC were observed in the marine aquaculture area off EGDC. The measured values of δ13CTOC, δ15NTN and the C/N ratio constrained the two dominant sources of organic matter, marine and terrestrial material, in the EGDC. Using a two end−member mixing model, we estimated the proportion of marine organic matter to be 74 ± 20% in spring, 74 ± 16% in summer and 75 ± 12% in winter, respectively. This suggests in situ production of marine phytoplankton dominated the organic carbon pool (average=~74%) in the EGDC sediment. These consistent marine organic matter contributions are also observed in sediment cores, with proportions averaging 59 ± 5% in core Y3 and 66 ± 4% in core Y5. The observed variations in biogeochemical proxies in sediments are thus mainly caused by differing relative contributions of marine and terrestrial organic matter. Additionally, the high TOC inventory in the marine aquaculture area indicates increased carbon storage and carbon fixation capacity compared to the other areas investigated. Overall, this study establishes a background carbon database in the coastal environment that can be used in future studies of blue carbon to achieve carbon neutrality in China.

Remote Sensing Classification of Offshore Seaweed Aquaculture Farms on Sample Dataset Amplification and Semantic Segmentation Model

Satellite remote sensing provides an effective technical means for the precise extraction of information on aquacultural areas, which is of great significance in realizing the scientific supervision of the aquaculture industry. Existing optical remote sensing methods for the extraction of aquacultural area information mostly focus on the use of image spatial features and research on classification methods of single aquaculture patterns. Accordingly, the comprehensive utilization of a combination of spectral information and deep learning automatic recognition technology in the feature expression and discriminant extraction of aquaculture areas needs to be further explored. In this study, using Sentinel-2 remote sensing images, a method for the accurate extraction of different algae aquaculture zones combined with spectral information and deep learning technology was proposed for the characteristics of small samples, multidimensions, and complex water components in marine aquacultural areas. First, the feature expression ability of the aquaculture area target was enhanced through the calculation of the normalized difference aquaculture water index (NDAWI). Second, on this basis, the improved deep convolution generative adversarial network (DCGAN) algorithm was used to amplify the samples and create the NDAWI dataset. Finally, three semantic segmentation methods (UNet, DeepLabv3, and SegNet) were used to design models for classifying the algal aquaculture zones based on the sample amplified time series dataset and comprehensively compare the accuracy of the model classifications for achieving accurate extraction of different algal aquaculture information within the seawater aquaculture zones. The results show that the improved DCGAN amplification exhibited a better effect than the generative adversarial networks (GANs) and DCGAN under the indexes of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The UNet classification model constructed on the basis of the improved DCGAN-amplified NDAWI dataset achieved better classification results (Lvshunkou: OA = 94.56%, kappa = 0.905; Jinzhou: OA = 94.68%, kappa = 0.913). The algorithmic model in this study provides a new method for the fine classification of marine aquaculture area information under small sample conditions.

TCNet: A Transformer–CNN Hybrid Network for Marine Aquaculture Mapping from VHSR Images

Precise delineation of marine aquaculture areas is vital for the monitoring and protection of marine resources. However, due to the coexistence of diverse marine aquaculture areas and complex marine environments, it is still difficult to accurately delineate mariculture areas from very high spatial resolution (VHSR) images. To solve such a problem, we built a novel Transformer–CNN hybrid Network, named TCNet, which combined the advantages of CNN for modeling local features and Transformer for capturing long-range dependencies. Specifically, the proposed TCNet first employed a CNN-based encoder to extract high-dimensional feature maps from input images. Then, a hierarchical lightweight Transformer module was proposed to extract the global semantic information. Finally, it employed a coarser-to-finer strategy to progressively recover and refine the classification results. The results demonstrate the effectiveness of TCNet in accurately delineating different types of mariculture areas, with an IoU value of 90.9%. Compared with other state-of-the-art CNN or Transformer-based methods, TCNet showed significant improvement both visually and quantitatively. Our methods make a significant contribution to the development of precision agricultural in coastal regions.

Vertical distribution and pollution assessment of TN, TP, and TOC in the sediment cores of cage farming areas in Dongshan Bay of southeast China

Cage farming is an important means of aquacultural production, while its potential environmental pollution needs to be further investigated. In this study, Dongshan Bay was taken as an example to investigate whether long-term cage farming in a semi-closed bay would cause environmental pollution via vertical distribution assessment. The four sediment cores (YB1, YB2, B1, and B2) were collected from two cage farming areas. Total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) were measured. The results showed a negative correlation between TN and TOC and the sediment depth at four sampling sites, and TP was also negatively correlated with the depth at three sites. The average TN (1,405.8, 1,413.8, 1,115.7, and 936.1 mgkg−1) and TP (1,206, 1,141.6, 1,064.6, and 932.8 mgkg−1) values of the four sites were markedly higher than the safety level, with the indexes STN, STP, FF, and ON of the four sites indicating moderate to severe pollution of nitrogen and phosphorus. Particularly, the YB1 and YB2 sites in the area with lower current speed have more severe pollution. The C/N ratio uncovered that the organic matter (OM) might mainly derived from the phytoplankton and nonfibrous plants as a result of excessive fish feed and feces. Collectively, the results indicated that long-term (more than 10 years) cage farming activity in a semi-closed bay such as Dongshan Bay had a negative impact on the environmental quality. Despite limitations in sample size and the absence of stable isotopic analyses, this study enhances our understanding of environmental changes and endogenous pollution risks in shallow marine aquaculture areas. Moreover, it suggests practical approaches such as implementing alternative farming and fallowing periods, should be conducted to mitigate the pollution.

Persentase Tutupan Karang Di Perairan Teluk Akle Kecamatan Semau Selatan Kabupaten Kupang Nusa Tenggara Timur

Akle Bay is one of marine aquaculture area in Semau island and it has coral reef potention. The coverage percentage of coral reef in the ocean might be decline by human activities and also by nature itself. The aimed of this research was analyzed to know the percentage of coral reef coverage in Akle Bay, South Semau District. This study conducted in Akle Bay, South Semau District on May 18, 2023st . Underwater Photo Transect (UPT) was used to take a sample in five meter depth at two different sites in Akle Bay waters. Data was analyzed by software CPCe 4.1. Result showed the highest benthic coverage percentage is soft coral. There were eleven genus of hard coral was found and its dominated by Porites genera. The percentage of hard coral coverage in S1 was 9.14%, while in S2 was 6.11% and was categorized in worst condition. The decline of coral reef coverage percentage in this area was expeted due to of human activities such as coral removal, the usage of anchor and the destructive fishing gear like a bomb, and also a sedimentation. According to the lifeform, covarage percentage of hard coral was dominated by coral branching. The average of current velocity in Akle Bay was 0,417 m/s with the range of waves was 0.1 to 0.5 meters. Conclusion of this research is the percentage of coral coverage in Akle Bay is worst. It was becacuse of human activities and sedimentation.

Evaluation of Eutrophication in Jiaozhou Bay via Water Color Parameters Determination with UAV-Borne Hyperspectral Imagery

The continued increase in greenhouse gas emissions as a result of unprecedented eutrophication has resulted in a rising trend of red tides in the sea, which may be responsible for ecological degradation in the surrounding environment. Studies rarely investigate the accurate concentration of seawater eutrophication substances in offshore aquaculture areas, which may lead to the exacerbated pollution of inshore aquaculture. We examined whether offshore seawater quality monitoring can be effectively performed through unmanned aerial vehicles’ (UAVs) airborne hyperspectral remote sensing technique at Jiaozhou Bay, a water body associated with eutrophication. We used the UAV airborne hyperspectral imager to detect and measure typical marine aquaculture areas in Jiaozhou Bay and selected the key parameters of seawater quality, chlorophyll-a (Chl-a) concentrations, and total suspended matter (TSM) concentrations as indicators of seawater eutrophication. The hyperspectral inversion model of the Jiaozhou Bay seawater (JZBZ) was established with the optimal sensitive band of parameters. Results showed that in comparison with the traditional inversion model, the inversion R2 of the Chl-a was 0.712, the RPD was 3.72, and the inversion R2 of the TSM concentration was 0.756 while the RPD was 5.83. We found that this model is more suitable for the retrieval of water color parameters in Jiaozhou Bay. Additionally, by actual measurement, it can be seen that the concentration ranges of Chl-a in the observation area are 0.380–1.74 mg/m3, and the concentration range of TSM is 12.6–131 mg/L. The results of this study indicate that the Jiaozhou Bay Water Quality Translation Model (JZBM), based on the UAV airborne hyperspectral imager, performs well in the inversion of the concentration of chlorophyll and suspended particulate matter in offshore water, which advances our understanding with a new method to assess the degree of eutrophication in coastal waters.

Solar-enhanced bio-electro-Fenton driven by sediment microbial fuel cell for ametryn degradation in simulated seawater

In marine aquaculture areas, herbicides have been used to inhibit the wild growth of seaweed, which may seriously affect the ecological environment and food safety. Here the commonly applied ametryn was used as the representative pollutant, and solar enhanced bio-electro-Fenton driven in situ by sediment microbial fuel cell (SMFC) was proposed to degrade ametryn in simulated seawater. SMFC with γ-FeOOH-coated carbon felt cathode was operated under the simulated solar light (γ-FeOOH-SMFC), where two-electron oxygen reduction and activation of H2O2 occurred to promote the production of hydroxyl radicals at the cathode. Hydroxyl radicals, photo-generated holes, and anodic microorganism worked together to degrade ametryn with an initial concentration of 2 mg/L in the self-driven system. The removal efficiency of ametryn in γ-FeOOH-SMFC was 98.7 % during the operation period of 49 days, which was 6 times higher than that under natural degradation condition. When γ-FeOOH-SMFC was in the steady phase, oxidative species were continuously and efficiently generated. The maximum power density (Pmax) of γ-FeOOH-SMFC was 44.6 W/m3. According to the intermediate products of ametryn degradation in γ-FeOOH-SMFC, four possible pathways of ametryn degradation were proposed. This study provides an effective, cost-saving, and in situ treatment for refractory organics in seawater.

Classification of Aquaculture Waters through Remote Sensing on the Basis of a Time-Series Water Index

Zhang, C.; Gao, L.; Lu, Z.; Liu, H.; Zhu, H., and Tang, K., 2022. Classification of aquaculture waters through remote sensing on the basis of a time-series water index. Journal of Coastal Research, 38(6), 1148–1162. Coconut Creek (Florida), ISSN 0749-0208. Accurate spatial distribution information on aquaculture is crucial for scientific aquaculture management, postdisaster evaluation, and water environment protection. At present, large-scale aquaculture areas can be quickly and accurately monitored using remote-sensing technology. However, most previous studies gave importance only to extracting aquaculture areas and ignored the classification of aquiculture types. Although several researchers classified aquaculture modes, the waters in aquaculture areas were unclassified. Moreover, classification of the aquaculture target is usually performed on a single remote-sensing image, which lacks time-series information. To examine existing issues, the current study selected the coastal marine aquaculture areas in the southern Lvushunkou District of Dalian. First, a normalized difference aquaculture water index (NDAWI) was constructed on the basis of the measured data through a spectral feature analysis. Second, NDAWI was extracted from Sentinel-2 images to construct a time-series data set, and the random forest classification method was applied to classify kelp and wakame aquaculture waters. Last, the experimental outcomes of the NDAWI data set were compared with those of other time-series data sets. Results showed that the overall accuracy of the NDAWI time-series data set was 92.11%, which showed an improvement of 7.68%, 17.66%, 4.50%, and 11.50% compared with the values for normalized difference chlorophyll index, normalized difference vegetation index, and greenness component of tasseled cap transformation data sets and single-phase images, respectively. The NDAWI time-series data set constructed in this study exhibited a dramatically improved classification accuracy compared with other time-series data sets. It has a high application value for identifying relevant aquaculture areas.

Monitoring Marine Aquaculture and Implications for Marine Spatial Planning—An Example from Shandong Province, China

Offshore marine aquaculture can not only provide a large amount of high-quality food for humans, but also effectively relieve the pressure on land space. However, it is difficult for traditional statistical data to reflect changes in spatial dynamics in marine aquaculture. It is also difficult to effectively manage marine space development given the current status of spatial planning regarding land–sea integration. This study used multiphase satellite remote sensing images of Shandong Province together with an automatic extraction algorithm for aquaculture to obtain spatial distribution data of marine aquaculture (surface of seawater visible by remote sensing, types of rafts, and cage aquaculture). GIS spatial overlay analysis technology was used to superimpose marine functional zoning (2010–2020) data for comparative analysis to evaluate implementation effectiveness and existing problems in marine functional zoning. Results showed that the critical time regarding substantial change in marine aquaculture area was around 2010, concurrent with the implementation of the current round of marine functional areas. The aquaculture area in the agricultural and fishery planning area increased from 228.33 km2 in 2010 to 344.6 km2 in 2018, and the overall proportion of aquaculture increased from 65.53% to 70.48%. This indicated that marine function planning can exert a guiding influence. The port area and protection area were found to be other major areas for the expansion of marine aquaculture. We also used field investigations in uncovering the phenomenon of the combined marine functions of marine aquaculture and tourism. On this basis, the role of spatial information technology in marine spatial planning was analyzed, which revealed the importance of coordinated integration of land–sea space for effective control of marine development.

Occurrence, distribution and risk assessment of antibiotics at various aquaculture stages in typical aquaculture areas surrounding the Yellow Sea

The pollution of antibiotics commonly existed throughout the entire aquaculture process, but the residues of antibiotics at different aquaculture stages have rarely been studied. This study investigated the occurrence, distribution and risk assessment of antibiotics at different aquaculture stages (the non-aquaculture stage, the early aquaculture stage, the middle aquaculture stage, and the late aquaculture stage) in two typical marine aquaculture areas (Mahegang River and Dingzi Bay) surrounding the Yellow Sea. Fluoroquinolones and tetracyclines were commonly used antibiotics in the aquaculture of these areas with high detection frequencies (17% to 83%). Compared among four aquaculture stages, the highest concentration of antibiotics (9032.08 ng/L) in aquaculture ponds was detected at the late aquaculture stage. And the antibiotic pollution level of natural water was directly related to the aquaculture stages. Similarly, at the aquaculture stages, the detection frequency of antibiotics in sediments was higher than that at the non-aquaculture stage. Based on the correlation analysis, the concentration of main antibiotics in water showed a positive correlation with total nitrogen (p<0.05) and chlorophyll a (p<0.01), while it showed a negative correlation with salinity (p<0.01) in coastal water of the Dingzi Bay. According to the risk assessment, with the development of aquaculture stages, the selection pressure of fluoroquinolones and tetracyclines on resistant bacteria had increased. And the ecological risks caused by sulfonamides and tetracyclines to aquatic organisms had also increased markedly. Overall, this study may provide a reference for formulating regulatory policies regarding antibiotic use at different aquaculture stages.

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

Research on Blade Design of Lift–Drag-Composite Tidal-Energy Turbine at Low Flow Velocity

The research on tidal-current energy-capture technology mainly focuses on the conditions of high flow velocity, focusing on the use of differential pressure lift, while the average flow velocity in most sea areas of China is less than 1.5 m/s, especially in the marine aquaculture area, where tidal-current energy is needed to provide green energy locally. Due to the low flow velocity of this type of sea area, it seriously affects the effect of differential pressure lift, which is conducive to exerting the effect of impact resistance. In this regard, the coupling effect of the differential pressure lift and the impact resistance on the blade torque is comprehensively considered, this research puts forward the design method of the lift-–drag-composite thin-plate arc turbine blade. Based on the blade element momentum (BEM) theory and Bernoulli’s principle, the turbine dynamic model was established, and the nonlinear optimization method was used to solve the shape parameters of the turbine blades, and the thin-plate arc and NACA airfoil blade turbines were trial-produced under the same conditions. A model experiment was carried out in the experimental pool, and the Xiangshan sea area in Ningbo, East China Sea was taken as the experimental sea area. The results of the two experiments showed the same trend, indicating that the energy-harvesting performance of the lift–drag-composite blade was significantly better than that of the lift blade under the conditions of low flow velocity and small radius, which verified the correctness of the blade design method, and can promote the research and development of tidal energy under the conditions of low flow velocity and small radius.

The potential study of marine aquaculture location in Eastern Bintan Island

Bintan is one of Indonesia’s islands, consisting of nearly 2408 small islands and directly adjacent to Singapore and Malaysia. The strategic geographic location and many small islands and supported by 95% of the sea area give Bintan Island an advantage in developing the marine aquaculture sector. This study aims to apply remote sensing analysis to collect comprehensive information on the strategic Marine Aquaculture Zone (MAZ) in the eastern part of Bintan Island. The data used in this study were the concentration of chlorophyll-a (Chl-a) and Sea Surface Temperature (SST) from MODIS level-3 with a resolution of 4 km from January to December 2019. The pre-processing data analysis on SST and Chl-a concentrations was used SeaDas software to ensure the results of the data projections in the form of the World Geodetic System 84 (WGS84) format. The data processing stage uses ER Mapper software to project the SST distribution and Chl-a concentration into a contour. Post-processing data analysis was used ArcGIS software to determine the results of crossing the SST contour and Chl-a combination to obtain the optimal potential location for the Marine Aquaculture Zone (MAZ). Image data processing shows the lowest potential marine aquaculture location was in Bintan Island in West Season with 202 MAZ. The highest potential location for marine aquaculture on Bintan Island in North Season with 584 MAZ. The results showed the potential location for marine aquaculture locations in Bintan Island was widely distributed, and most of the locations far from the coastline of eastern Bintan Island. This study indicates that the east part of Bintan Island has the potential to be implemented for marine aquaculture areas to develop sustainable marine aquaculture production.

A new satellite-derived dataset for marine aquaculture areas in China's coastal region

Abstract. China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed model based on deep convolution neural networks (CNNs) and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy &gt; 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ∼ 1100 km2 (1096.8–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % is marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz file format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).