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

Environmental DNA reveals how the absolute dominant zooplankton species affects the community

Zooplankton play a crucial role in marine ecosystems. In recent years, the East China Sea (ECS) has recorded the highest levels of pollution among China's marine areas, leading to a trend toward homogenization of the dominant zooplankton species. Environmental degradation has triggered various ecological responses, but how these responses further impact community structure requires more investigation. In this study, we employed a combined approach of morphological identification and environmental DNA (eDNA) metabarcoding to analyze the effects of seasonal variation and dominant species on zooplankton communities. The results indicated that an absolute dominant species, Calanus sinicus, emerged in the ECS, significantly occupying ecological resources. As seasons changed, C. sinicus showed a trend of migrating from offshore to coastal areas. The high abundance of C. sinicus in the sub-regions led to a regional decline in zooplankton community diversity and interspecies cooperation, while the community also experienced stronger dispersal limitations. Furthermore, the spring zooplankton community exhibited higher alpha and beta diversity, as well as a more stable co-occurrence network compared to summer. However, the level of interspecies cooperation in spring was lower than in summer. In conclusion, the high abundance of C. sinicus has a significant impact on zooplankton communities, and this impact shows a lag effect.

Nutrients transport behavior in inlet river in the Yellow River Delta in winter

The nutrients such as dissolved inorganic nitrogen (DIN, NH4+-N, NO2--N, and NO3--N), dissolved inorganic phosphorus (DIP, PO43-) and dissolved SiO2 (DSi) funneled by the inlet river are the dominant factors to coastal eutrophication. This study investigated nutrient transport process in typical inlet rivers in the Yellow River Delta. The indicator of coastal eutrophication potential and concentration ratio between upstream and downstream stations were used to evaluate the influence of different sources to the nutrient risks. It showed that urban areas are the most important source of the nutrients in studied rivers. The harbor and mariculture would have greater risk because of their proximity close to the coastal area. Wetland was a vital conversion to eliminate the river nutrients, and the retention could reach 80 %. It is imperative to protect and construct wetlands to reduce the nutrient pollution in the inlet river.

Disaster management and environmental policy integration in Pakistan - an evaluation with particular reference to the China-Pakistan Economic Corridor Plan

Economic and social development, the state of the environment and a propensity for disasters are closely intertwined. Therefore, environmental policy integration (EPI) across development and disaster management (DM) policies and plans is important. Pakistan as a country is highly vulnerable to climate-induced environmental changes and associated disasters. In this paper, the extent to which its national environment and climate change policy, disaster risk reduction (DRR) policy as well as disaster management (DM) plans are aligned is established, based on a review of government documents and expert opinions. In this context, a particular emphasis is put on China's Belt and Road Initiative (BRI) that led the China-Pakistan Economic Corridor Plan (CPEC; 2017-2030). While environmental assessment (EA) is currently not conducted for any DM policies and plans, DM and EA are well integrated into provincial environmental protection acts, in national as well as most provincial DM plans. It is concluded that a regulatory framework to guide EPI in DM for BRI and CPEC projects is needed.

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

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.

Potential sources and toxicity risks of polycyclic aromatic hydrocarbons in surface sediments of commercial ports in Taiwan

The accumulation of pollutants in the semi-enclosed waters of ports has long been a concern. This study assessed the pollution status, sources, and toxicity risks of 16 polycyclic aromatic hydrocarbons (PAHs) in surface sediments of 7 major ports in Taiwan. Total PAHs concentrations in sediments ranged between 8.4 and 572.5 ng/g dw, with an average of 112.4 ± 136.5 ng/g dw. The 3- and 4-ring PAHs (63 %) were the major constituents of PAHs in the sediments. Diagnostic ratios and positive matrix factorization analyses indicated that PAHs in sediments were mainly contributed by biomass combustion (45.0 %), coal combustion (31.5 %), and vehicle emissions and related fossil fuels (23.5 %). The results of ecological risk assessment showed a low-medium risk of PAHs in the sediments outside the port, whereas most of the sediments within the port presented a medium-high risk. An assessment of the possible human health risks indicated that PAHs were present at acceptable levels.