Evaluating Soil and Sediment Quality in Coastal Regions for Sustainable Aquaculture Development

Year 2025, Volume: 10 Issue: 2, 393 - 401, 01.09.2025

Nagarajan Muthu , Arasu Sathiyamurthy

https://doi.org/10.28978/nesciences.1714427

Abstract

The sustainable development of fish farming activities and coastal zone aquaculture is crucially dependent upon soils and sediments, as they greatly influence the species' water quality, health, and productivity. This investigation focuses on physicochemical characteristics of soils and sediments in several coastal areas to determine their potential for best sustainable aquaculture practices. Specifically, this study focuses on pH, salinity, organic matter, nitrogen, phosphorus, heavy metals, and sediment texture. Samples from different focused aquaculture areas were collected and tested in certified laboratories, with results subjected to statistical processing. The study results revealed favorable conditions for aquaculture in regions with a balanced pH of 7.0-8.5, moderate organic matter content, and low heavy metals concentrations. Some other areas have elevated concentrations of overseas cadmium and lead due to industrial effluents and human activities, which pose a significant danger to aquatic life and ecosystem sustainability. Cadmium and lead settle on marine organisms and vector them through the food chain, affecting the ecosystem. Analysis of sediment textures showed that ponds with enriched nutrients and silty clay have better productive capacity, while sandy soils require other amendments to achieve ideal productivity. It is suggested that active monitoring of soil and sediment in coastal zones of aquaculture must be boosted. It also recommends incorporating best management practices (BMPs) like organic matter and pollution abatement enhancement to protect soil health and environmental sustainability. These conclusions are valid for policy formulation, as well as for site selection, and farming practices directed towards environmental conservation and food security. This study assists in rational planning for sustainable aquaculture in coastal areas by providing an ecological basis through the thorough assessment of soil and sediment quality for environmental and economic sustainability.

Keywords

Soil quality , Sediment analysis , Coastal aquaculture , Environmental sustainability , Heavy metals , Nutrient dynamics , best management practices

References

• Agriculture Organization of the United Nations. Fisheries Department. (2018). The state of world fisheries and aquaculture. Food and Agriculture Organization of the United Nations.
• Ahmed, N., & Glaser, M. (2016). Coastal aquaculture, mangrove deforestation and blue carbon emissions: Is REDD+ a solution?. Marine Policy, 66, 58-66. https://doi.org/10.1016/j.marpol.2016.01.011
• Alongi, D. M. (2012). Carbon sequestration in mangrove forests. Carbon management, 3(3), 313-322. https://doi.org/10.4155/cmt.12.20
• Basnet, R. B., Shash, R., Johnson, C., Walgren, L., & Doleck, T. (2019). Towards Detecting and Classifying Network Intrusion Traffic Using Deep Learning Frameworks. Journal of Internet Services and Information Security, 9(4), 1-17.
• Biswas, D., & Tiwari, A. (2024). Utilizing Computer Vision and Deep Learning to Detect and Monitor Insects in Real Time by Analyzing Camera Trap Images. Natural and Engineering Sciences, 9(2), 280-292. https://doi.org/10.28978/nesciences.1575480
• Böhm, L., Jakobi, C., & Schwabe, F. (2021). Heavy metal contamination in aquaculture: Sources and environmental impact. Environmental Pollution, 273, 116530.
• Boyd, C. E. (2003). Pond aquaculture water quality management. Springer Science & Business Media.
• Boyd, C. E., & Tucker, C. S. (2012). Pond aquaculture water quality management. Springer Science & Business Media.
• Boyd, C. E., McNevin, A. A., & Tucker, C. S. (2017). Principles and practices of aquaculture water quality management. Springer.
• Carvalho, A. A., Karthikeyan, K., Clement Sudhahar, J., & Jesiah, S. (2024). Risk assessment and decision-making in geology-driven projects: a management perspective. Archives for Technical Sciences, 2(31), 329–339. https://doi.org/10.70102/afts.2024.1631.329
• Food and Agriculture Organization (FAO). (2020). The State of World Fisheries and Aquaculture 2020: Sustainability in action. Rome. https://doi.org/10.4060/ca9229en
• Johnson, C., Khadka, B., Basnet, R. B., & Doleck, T. (2020). Towards Detecting and Classifying Malicious URLs Using Deep Learning. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 11(4), 31-48.
• Kumar, P., & Singh, R. K. (2019). Data-driven models for sustainable aquaculture: Applications in coastal regions. Environmental Monitoring and Assessment, 191(12), 734.
• Kumar, P., Tiwari, A., & Singh, R. K. (2020). Sediment management for sustainable coastal aquaculture: A case study from India. Environmental Monitoring and Assessment, 192(5), 322.
• Liu, Y., Zhang, X., & Zhao, W. (2015). Effects of sediment texture on aquaculture pond water quality and productivity. Aquaculture Research, 46(8), 1940–1949.
• Mishra, N., Haval, A. M., Mishra, A., & Dash, S. S. (2024). Automobile Maintenance Prediction Using Integrated Deep Learning and Geographical Information System. Indian Journal of Information Sources and Services, 14(2), 109–114. https://doi.org/10.51983/ijiss-2024.14.2.16
• Mohamed, R. A., El-Sherbiny, M. M., & Wassef, A. S. (2017). Impacts of nutrient-rich sediments on water quality and productivity in coastal aquaculture. Marine Pollution Bulletin, 115(1-2), 355–364.
• Nallasivamani, P., Nishanth, R., Rameshkrishnan, M., Soundar, M., & Mounika, S. (2023). Clothing Recommender System using Deep Learning. International Journal of Advances in Engineering and Emerging Technology, 14(1), 58–63.
• Rahman, M. M., Hossain, M. Z., & Rahman, M. S. (2019). Assessment of soil and water quality for sustainable aquaculture in south-western coastal region of Bangladesh. Journal of Applied Aquaculture, 31(4), 316–333.
• Ranjkesh, M., & Ziabari, M. (2016). Persian Language telephone and Microphone Speaker identification using neural networks. International Academic Journal of Science and Engineering, 3(2), 6–12.
• Sofiazizi, A., & Kianfar, F. (2015). Modeling and Forecasting Exchange Rates Using Econometric Models and Neural Networks. International Academic Journal of Innovative Research, 2(1), 49–65.
• Wang, J., Li, H., & Yang, L. (2018). Heavy metals in aquaculture sediments: Risk assessment and management. Environmental Toxicology and Chemistry, 37(7), 1962–1970.
• Wang, Z., Liu, G., & Zhang, X. (2020). Application of machine learning models for predicting sediment quality in aquaculture systems. Aquaculture Research, 51(7), 2720–2728.
• Zhou, J., Wu, Y., Wang, W., & Liu, H. (2017). Effects of sediment organic matter and nutrients on bacterial communities in the coastal aquaculture environment. Marine Pollution Bulletin, 117(1-2), 259–267.