Journal of Fisheries Research

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Mini Review - Journal of Fisheries Research (2024) Volume 8, Issue 3

Aquatic Toxicology: Assessing the Impact of Contaminants on Marine Life

Cong Zhang*

Department of Aquatic and Fishery Sciences, Ningbo University, Ningbo, China

*Corresponding Author:
Cong Zhang
Department of Aquatic and Fishery Sciences
Ningbo University
China
E-mail:cong@zhang.cn

Received:03-Jun-20234,Manuscript No.AAJFR-24-140117; Editor assigned:04-Jun-2024,PreQC No. AAJFR-24-140117(PQ); Reviewed:18-Jun-2024,QC No.AAJFR-24-140117; Revised:22-Jun-2024,Manuscript No.AAJFR-24-140117(R); Published:30-Jun-2024,DOI: 10.35841/aajfr-8.3.206

Citation: Zhang C. Aquatic Toxicology: Assessing the Impact of Contaminants on Marine Life. J Fish Res. 2023;8(3):206

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Introduction

In order to supply the growing demand for seafood worldwide while also pursuing sustainability, the aquaculture sector is vital. This study looks at new developments in fishing technology that are meant to improve aquaculture methods that are sustainable. The study explores automation, data analytics tools, and sophisticated monitoring and control systems that help aquaculture companies become more efficient, less harmful to the environment, and better at managing their resources.[1].

Aquaculture has changed dramatically as a result of the convergence of artificial intelligence, Internet of Things, and remote sensing. Real-time monitoring of fish behavior, environmental circumstances, and health status is made possible by these technologies, which offer insightful information for preemptive decision-making. Blockchain-based solutions help to make supply chains more transparent and solve issues with accountability and traceability in the seafood sector. The study also looks into the application of robotics and automation in fish farming, examining the ways in which these technologies might minimise the environmental impact of aquaculture operations, optimise feeding techniques, and cut waste. Furthermore, fish movement research has never been easier thanks to developments in biotelemetry and satellite tagging for fisheries. These developments have improved our knowledge of fish migration patterns and habitat use.[2].

The study also looks at how 3D printing might be used to create specialised gear and equipment that would meet the unique requirements of various aquaculture settings. The potential of digital twin technology to build virtual representations of aquaculture systems is being investigated in order to facilitate simulation and optimisation prior to deployment. The goal of this study is to present a thorough analysis of current technology advancements in fishing methods, highlighting their contribution to environmental sustainability. Due to shifting dietary habits and population expansion, there has been an unparalleled surge in the demand for seafood worldwide. Aquaculture has become a vital part of supplying the growing demand while aiming for sustainability as capture fisheries confront more difficulties including overfishing and environmental deterioration. In this regard, addressing the intricate dynamics of aquaculture has grown dependent upon the incorporation of cutting-edge technologies into fishery methods.[3].

The goal of this study is to examine how current advancements in fishing technology can improve aquaculture practices while also contributing to sustainability. Achieving sustainability in aquaculture requires striking a careful balance between minimising the negative environmental effects of fish farming and providing for the dietary needs of an expanding population. Technological developments present viable answers to this problem by giving us instruments for better resource management, environmental monitoring, and operational effectiveness. Aquaculture system monitoring and management have been completely transformed by the combination of artificial intelligence, Internet of Things (IoT) sensors, and remote sensing technology. A proactive approach to addressing problems with water quality, fish health, and overall system performance is made possible by real-time data gathering and analysis. This helps to minimise negative environmental effects while also improving the efficiency of aquaculture operations.[4].

The seafood business has noticed a rise in the use of blockchain technology because of its built-in traceability and transparency characteristics. This study looks into how blockchain technology might help create transparent aquaculture supply chains, guarantee the provenance of seafood, and solve issues with unreported, illicit, and unregulated (IUU) fishing. The study also looks at how automation and robotics are changing fish farming methods. In addition to increasing operational effectiveness, automation in feeding systems, waste management, and environmental control helps aquaculture operations leave a less environmental impact. The potential benefits to our knowledge of fish behaviour, migration patterns, and habitat utilisation via fishery satellite tagging, biotelemetry, and other tracking technologies is discussed. Furthermore, the study explores the new uses of 3D printing in aquaculture, especially for producing specialised gear and equipment. The potential of digital twin technology to build virtualized aquaculture systems is being investigated in order to enable simulation and optimisation prior to deployment. This study intends to shed light on these cutting-edge technologies' potential to promote sustainability in the aquaculture industry through a thorough investigation. Stakeholders may contribute to a future in which aquaculture not only satisfies the growing demand for seafood but also maintains the health of aquatic ecosystems and the industry's long-term viability by comprehending and using these technological breakthroughs.[5].

References

References

  1. Abrigo MR, Love I. Estimation of panel vector autoregression in Stata. Stata J. 2016;(3):778-804.


    2. Google scholar

  2. Alvarez J, Arellano M. The time series and cross?section asymptotics of dynamic panel data estimators. Econometrica. 2003;( 4):1121-59.


    3. Google scholar

  3. Bi M, Zhang Z, Guo X, et al. Evaluation of Sustainable Utilization of African Marine Fishery Resources. Fishes. 2022;(1):4.


    4. Google scholar

  4. Bondad-Reantaso MG, Subasinghe RP, Josupeit H, et al. The role of crustacean fisheries and aquaculture in global food security: past, present and future. J Invertebr Pathol. 2012;110(2):158-65.


    5. Indexed at,Google scholar,Cross ref

  5. Fu XM, Wu WY, Lin CY, et al. Green innovation ability and spatial spillover effect of marine fishery in China. Ocean Coast Manag. 2022;228:106310.


    6. Google scholar

  6. Han LM, Li DH. Blue food system: Guarantee of China’s food security. Issues Agric. Econ. 2015;36:24-9.


    7. Google scholar

  7. Jennings S, Kaiser MJ. The effects of fishing on marine ecosystems. Adv Mar Biol. 1998;34: 201-352.


    8. Google scholar,

  8. Ji J, Li Y. The development of China's fishery informatization and its impact on fishery economic efficiency. Marine Policy. 2021;133:104711.


    9. Google scholar

  9. Sigmund M, Ferstl R. Panel vector autoregression in R with the package panelvar. Q Rev Econ Finance. 2021; 80:693-720.


    10. Google scholar

  10. Tu?an M. Panel VAR models with interactive fixed effects. J. Econom.2021;24(2):225-46.


    11. Google scholar

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