Impact of Port and harbour activities on plankton distribution and dynamics: A multivariate approach

Shaikh, Siratun Montaha S.; Tagde, Jyoti; Singh, Pooja R.; Dutta, Smita; Sangolkar, Lalita N.; Kumar, Manish

doi:10.1016/j.marpolbul.2021.112105

Cited by 13 publications

(3 citation statements)

References 43 publications

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“…When ballast water is released, the response of the phytoplankton community may be impacted due to the introduction of non-native species bringing about changes to the whole ecosystem and marine resources [65]. These uncertainties increase with tropical temperatures and port activities [66]. While zooplankton increases with phytoplankton to some extent [67] and provides food for fish, thereby encouraging marine life, increased growth of phytoplankton reduces the available oxygen levels in the water [68], thereby impacting the survival of marine life.…”

Section: Water Qualitymentioning

confidence: 99%

Oceanic Environmental Impact in Seaports

Agarwala,

Saengsupavanich

2023

Oceans

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Seaports are gateways that connect a nation to the world economy. With trade by sea increasing due to globalization, the need for the improvement and development of seaports cannot be overlooked. While the development of ports is considered essential for the economic growth and prosperity of a nation, they also result in environmental deterioration that can hurt the future of humanity. The factors that cause such deterioration are many and have been discussed and studied in some detail over the years. Of these, however, studies associated with the environmental impacts of seaports emanating from the oceanfront are limited. It is with this understanding that the current work discusses the physical and biological impacts that occur due to the oceanic environment in seaports, the existing policy provisions, and the possible ways ahead to reduce environmental deterioration and allow their sustainable operation, by means of reviewing published works.

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Section: Water Qualitymentioning

confidence: 99%

Oceanic Environmental Impact in Seaports

Agarwala,

Saengsupavanich

2023

Oceans

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“…Figure 8d-f,j-l depict that the nutrient concentration around the port was significantly higher than in the surrounding sea. This has its cause in the accumulation of nutrient sources or pollutants through discharges from high-intensity port vessel activities, loading and unloading processes, and dredging [52]. It induces the eutrophication of water [53], what ultimately leads to algal blooms [54,55].…”

Section: Impact Of Urbanizationmentioning

confidence: 99%

Inversion and Driving Force Analysis of Nutrient Concentrations in the Ecosystem of the Shenzhen-Hong Kong Bay Area

Zhang

Zhu

et al. 2022

Remote Sensing

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Although satellite remote sensing technology is intensively used for the monitoring of water quality, the inversion of coastal water bodies and non-optically active parameters is still a challenging issue. Few ongoing studies use remote sensing technology to analyze the driving forces of changes in water quality from multiple aspects based on inversion results. By the use of Landsat 5/8 imagery and measured in situ data of the total nitrogen (TN) and total phosphorus (TP) in the Shenzhen-Hong Kong Bay area from 1986 to 2020, this study evaluated the modeling effects of four machine learning methods named Tree Embedding (TE), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Back-propagation Neural Network (BPNN). The results show that the BPNN creates the most reliable and robust results. The values of the obtained correlation coefficients (r) are 0.83, 0.92, 0.84, and 0.90, and that of the coefficients of determination (R2) are 0.70, 0.84, 0.70, and 0.81. The calculated mean absolute errors (MAEs) are 0.41, 0.16, 0.06, and 0.02, while the root mean square errors (RMSEs) are 0.78, 0.29, 0.12, and 0.03. The concentrations of TN and TP (CTN, CTP) in the Shenzhen Bay, the Starling Inlet, and the Tolo Harbor were relatively high, fluctuated from 1986 to 2010, and decreased significantly after 2010. The CTN and CTP in the Mirs Bay kept continuously at a low level. We found that urbanization and polluted river discharges were the main drivers of spatial and inter-annual differences of CTN and CTP. Temperature, precipitation, and wind are further factors that influenced the intra-annual changes of CTN and CTP in the Shenzhen Bay, whilethe expansion of oyster rafts and mangroves had little effect. Our research confirms that machine learning algorithms are well suited for the inversion of non-optical activity parameters of coastal water bodies, and also shows the potential of remote sensing for large-scale, long-term monitoring of water quality and the subsequent comprehensive analysis of the driving forces.

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“…As a major hub for bulk cargo transportation, ports play an important role in the comprehensive transportation network [1]. Under the background of the initiative of "The Belt and Road", the shipping industry in China has developed rapidly, while a consensus has been reached on the significance of water quality monitoring in the port waters.…”

Section: Introductionmentioning

confidence: 99%

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

Guo,

Jiang,

Chu

et al. 2024

JMSE

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With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

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Impact of Port and harbour activities on plankton distribution and dynamics: A multivariate approach

Cited by 13 publications

References 43 publications

Oceanic Environmental Impact in Seaports

Oceanic Environmental Impact in Seaports

Inversion and Driving Force Analysis of Nutrient Concentrations in the Ecosystem of the Shenzhen-Hong Kong Bay Area

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

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