This study assessed the potential of the New Zealand mud snail Amphibola crenata to act as a bioindicator of contaminated estuarine sediment. Seventeen sites with varying contaminant burdens were identified within six New Zealand regions. Attributes (population density, individual length distribution and individual dry weight condition index) were measured for field-collected A. crenata, and related to measurements of sediment trace metals and nutrients. Population density of the mud snail was relatively high in sites with elevated nutrients and organic matter. The length distribution of A. crenata showed significant regional and site-specific variations. Minimum, mean, and median shell length of A. crenata were positively correlated with sediment cadmium and zinc concentration. Overall, the sites were able to be distinguished by A. crenata population attributes and the sediment metal and nutrient content. These results suggest that A. crenata population information has potential value for assessing estuarine sediment metal and nutrient contamination.

The air pollution in Jakarta has been recorded regularly; meanwhile, the information of atmospheric microplastics is still unknown. This study examines the characteristics (shape, size, and polymer) and deposition rate of atmospheric microplastics in Jakarta. The sample was obtained by putting a rain gauge for 12 months. All microplastic samples were analyzed for polymer using FT-IR. The lowest to the highest percentage of atmospheric microplastic based on shape were foam<fragment<fiber, meanwhile based on size were of 500–1000 μm < 300–500 μm. The detected polymers included polyester, polystyrene, polybutadiene, and polyethylene. The microplastics deposition rate ranged from 3 to 40 particles m⁻²d⁻¹, with an average of 15 particles m⁻²d⁻¹. The rainy season's deposition rate (23.422 particles m⁻²d⁻¹) was higher than the dry season (5.745 particles m⁻²d⁻¹). Our study proves that the atmospheric microplastic exists in Jakarta's air and needs to be considered to monitor by the government regularly.

A coupled physical-biogeochemical model is used to investigate the seasonal evolution and controlling factors of oxygen depletion in the Bohai Sea (BS). Comparisons show that the model reproduces observed spatiotemporal variations of important physical and biogeochemical variables well. Bottom oxygen in the BS shows an annual cycle with significant drawdown in summer and enhanced replenishment in fall. Two oxygen-depleted regions off Qinhuangdao (QHD) and the Yellow River estuary (YRE) develop separately and experience higher oxygen depletion rates and longer durations of low-oxygen conditions. The evolution of oxygen depletion is primarily controlled by stratification and biological oxygen consumption but is also modulated by lateral transport. Strong stratification is established earlier than oxygen depletion and maintains its development. The biological oxygen consumption determines the two oxygen-depleted regions under stratified conditions. Lateral transport influenced by anticyclonic circulations favors an expansion of oxygen depletion off QHD but alleviates oxygen depletion off the YRE.

Marine debris, directly and indirectly, threatens marine habitat and biota. Fishing activity is generally recognised as a contributor to marine debris, but the relative input from recreational fishing remains unassessed. Here we provide the first comprehensive literature review of recreational fishing marine debris (RFMD) on a global scale. A systematic literature review identified 70 studies related to RFMD, and plastic and metal respectively were the dominant debris materials found. Nearshore coastal areas and reefs, acted as both sources and sinks of RFMD and a diverse suite of potential impacts such as ghost fishing and entanglement were identified at local scales. Overall, research of RFMD is lacking globally, however, its role in marine debris input is likely underestimated. We recommend more research on the volumes and risks, using a standardised classification approach. Where intervention is required, we suggest cooperative approaches between the sector and authorities.

The Yellow Sea Bottom Cold Water zone (YSBCW) is a unique seasonal phenomenon in the Yellow Sea, where sea-floor cold water formed in winter is maintained until summer. This survey was conducted at 36 sites from 2018 to 2020. We identified 130 species of polychaetes, with an average density of 275 individuals/m². The number of species and density were different outside and inside of the YSBCW, and the outside was generally high. The remaining dominant species were all deposit feeders, although differences were observed in the surface or subsurface (burrowers). Correlations between polychaete community and environmental variables strongly correlated with depth, temperature, gravel, and sand. This study investigated polychaete community distribution, environmental characteristics, and feeding guilds in the YSBCW and can be used as a basic database for comprehensive research related to the Yellow Sea in the future.

An analytical framework was proposed for analyzing long-term chemical pollution in a coastal region with limited environmental data. The framework consists of compiling and synthesizing the available knowledge including the chemical's properties and the environmentally relevant data, as well as the data obtained by past monitoring studies. The gathered data is analyzed to assess multimedia fate of the pollutant by using fugacity-based intermedia transport calculations. Uncertainty analysis by applying Monte Carlo simulations is an integrated part of the framework. Dispersion factor (k) values were adopted, enabling a unified and intuitive way to define lognormal uncertainty distributions. The proposed framework was applied to polycyclic aromatic hydrocarbon (PAH) pollution in Izmit Bay, a coastal region in Turkey, impacted by industrialization and population growth. The analysis showed the importance of atmospheric pollution as a PAH source and indicated that Izmit Bay sediments may be at steady state for most PAHs.

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life.

The presence and effects of microplastics in the environment is being continuously studied, so the need for a reliable approach to ascertain the polymer/s constituting them has increased. To characterize them, infrared (IR) spectrometry is commonly applied, either reflectance or attenuated total reflectance (ATR). A common problem when considering field samples is their weathering and biofouling, which modify their spectra. Hence, relying on spectral matching between the unknown spectrum and spectral databases is largely defective. In this paper, the use of IR spectra combined with pattern recognition techniques (principal components analysis, classification and regression trees and support vector classification) is explored first time to identify a collection of typical polymers regardless of their ageing. Results show that it is possible to identify them using a reduced suite of spectral wavenumbers with coherent chemical meaning. The models were validated using two datasets containing artificially weathered polymers and field samples.