Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.

Floating marine litter (FML) surveys were conducted in the near shore waters of Mombasa, Kilifi and Kwale Counties of Kenya through trawling using a manta net. A mean density of 26,665 ± 2869 items km⁻² composed of 34.8% hard plastic, 40.5% soft plastics and 22.0% plastic lines/fibers was reported in this study. Litter densities in Kwale, Kilifi and Mombasa Counties were not influenced by monsoons, however, litter composition was influenced by monsoons with NEM and SEM being dominated mainly by hard plastics and soft plastics respectively. Litter categories diversity, evenness and richness were also not influenced by the monsoons during both NEM (1.01, 0.78 and 3, respectively) and SEM (1.09, 0.78 and 4, respectively). Fishing and recreational beaches had higher litter densities during NEM compared to SEM attributed to higher beach visitation and increased fishing activities during the calmer NEM season.

The purposes of this study were to measure the concentrations of arsenic speciation in shellfish from South China Sea and evaluate the health risk by local residents through shellfish consumption. The median concentrations (in wet weight) of arsenic speciation in shellfish samples were in the following order: AsB (16.0 mg·kg⁻¹) > DMA (1.30 mg·kg⁻¹) > AsV (0.23 mg·kg⁻¹) > AsC (0.08 mg·kg⁻¹) > AsIII (0.05 mg·kg⁻¹) > MMA (0.01 mg·kg⁻¹). Among shellfish species, Mactra mera and Babylonia areolata were found to accumulate iAs and AsB, respectively. The target hazard quotient values of iAs (THQᵢAₛ) in all shellfish samples were lower than 1. However, the carcinogenic risk values of iAs (CRᵢAₛ) in the Mactra mera, Mytilus galloprovincialis and Pinctada margaritifera were beyond the acceptable range, implying that continuous exposure to iAs pollution via the consumption of these shellfish would pose a potential cancer risk to local consumers.

Mangrove forests have been reported as sinks for metals because of the immobilization of these elements in their soils. However, climate change may alter the functioning of these ecosystems. We aimed to assess the geochemical dynamics of Mn, Cu, and Zn in the soils of a mangrove forest dead by an extreme weather event in southeastern Brazil. Soil samples were collected from dead and live mangroves adjacent to each other. The physicochemical parameters (total organic carbon, redox potential, and pH), total metal content, particle size, and metal partitioning were determined. Distinct changes in the soil geochemical environment (establishment of suboxic conditions) and a considerable loss of fine particles was caused by the death of the mangroves. Our results also showed a loss of up to 93 % of metals from soil. This study highlights the paradoxical role of mangroves as potential metal sources in the face of climate change.

Seventy-four surface sediment samples were collected from the Arctic Ocean and Bering Sea to determine the content of metal(loid)s (As, Cu, Cd, Ni, Pb, Zn and Cr). Metal(loid)s content in these sediments varied from 2.36–41.90 mg/kg for As, 8.63–82.28 mg/kg for Cu, 0.14–0.71 mg/kg for Cd, 11.86–100.60 mg/kg for Ni, 8.30–27.58 mg/kg for Pb, 39.93–391.43 mg/kg for Zn, and 40.96–106.49 mg/kg for Cr. The pH and water-soluble organic carbon content had considerable impacts on the content of metal(loid)s in sediment, but the texture of sediment has limited influence on metal(loid)s content in sediment. In addition, the hotspots of most of these metal(loid)s appeared in the Beaufort Sea region. The geoaccumulation index (Igₑₒ) indicated that Cd was the metal with the highest contamination in these sediments, with 55.41% of the sample sites posing moderate pollution. The ecological risk for As, Cu, Ni, Pb, Zn and Cr indicates low ecological risk (100%), while Cd posed moderate risk (35.14%), considerable risk (54.05%) and high risk (10.81%) and attributed more than 76.45% of the total potential ecological risk of these metal(loid)s.

Marine litter is recognised as imposing a range of costs on marine economies and environments as we target UN Sustainable Development Goals (SDGs) to 2030. Prevention of these avoidable damage costs can restore economic benefits and ocean health. In the Asia-Pacific we estimate the annual damage cost from marine litter to the marine economy has risen eightfold since 2008 and in 2015 was US$10.8 billion (bn), translating to $18.3bn globally ($21.3bn in 2020). In 2020 the present value of global economic damage costs to 2030 and 2050 are $−197bn and $−434bn respectively and as high as $−229bn and $−731bn, if predicted increased plastic production eventuates. As avoidable costs, these projections are the unacceptable “cost of global inaction” in today's terms. Litter prevention by government, industries and the community is needed now, to reduce these predicted marine economic cost impacts to 2050 with environmental benefits.

A quantification of total mercury (Hgₜₒₜ) and methylmercury (MeHg) concentrations in sediment and mussels was carried out in the east basin of the Orbetello lagoon in order to assess their bioaccumulation potential. The sediment was sampled in four macroareas, mussels were transplanted in the same sites and collected after seven weeks. The results show that Hgₜₒₜ concentrations in sediments exceeded (0.21–16.9 mg/kg dry weight (dw)) the environmental quality standard of the Italian legislation (0.3 mg/kg dw). The Hgₜₒₜ concentration in mussels (0.050–0.324 mg/kg wet weight (ww)) does not exceed the limit values (0.5 mg/kg ww) of the European food legislation. The biota–sediment accumulation factors (BSAFs) derived for MeHg (80–306.7) and a biomagnification factor (BMF) greater than 1 for Hgₜₒₜ demonstrate that in the lagoon, these compounds can be transferred in the upper levels of the trophic chain and pose a risk to human health.

Microplastics (MPs) in soil and sediment (SS) matrices are emerging pollution hazards to ecosystems and humans. To mitigate MP pollution, suitable extractors and associated extracting solutions are required to efficiently separate MPs from SS matrices. In this study, we introduced a four-stage microplastic extractor (ME) device and investigated the fractional separation efficiencies of three extracting solutions (ultrapure water, saturated NaCl, and corn oil-in-NaCl) plus aeration, magnetic stirring, and electric stirring for three kinds of SS matrices (loam soil, sandy sediment, and muddy sediment) with four types of virgin MP pellets (acrylonitrile butadiene styrene (ABS), polycarbonate (PC), polypropylene, and polystyrene). In addition, fragments of these four types of post-consumer MPs were also tested by the ME device. The mean recovery efficiencies of these MPs in the three SS matrices were 88.3 %–100 %. Oil-in-NaCl further improved the recovery efficiencies for the denser ABS and PC up to 40 % based on NaCl extraction.

Aquaculture, also known as aqua farming, is defined as farming fish, crustaceans, mollusks, aquatic plants, algae, and other marine organisms. It includes cultivating fresh- and saltwater populations under controlled conditions compared to commercial fishing or wild fish harvesting. Worldwide, carp, salmon, tilapia, and catfish are the most common fish species used in fish farming in descending order. Disinfectants prevent and/or treat different infections in aquatic animals. The current review indicates the uses of different disinfectants against some important pathogens in aquaculture, with particular reference to tilapia (Oreochromis niloticus) farming. A single review cannot cover all aspects of disinfection throughout aquaculture, so the procedures and principles of disinfection in tilapia farming/aquaculture have been chosen for illustration purposes.

The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Additionally, different concentration oil data were used to obtain a concentration-estimation model. In the classification, K-Nearest Neighbor (KNN) obtained the best approximations in oil detection using Blue (0.453–0.520 μm), NIR (0.790–0.891 μm), SWIR1 (1.557–1.717 μm), and SWIR2 (1.960–2.162 μm) bands for 2010 spill images. In the concentration model, the mean absolute error (MAE) was 1.41 and 3.34, for training and validation data. When testing the concentration-estimation model in images where oil was detected, the concentration-estimation obtained was between 40 and 60 %. This demonstrates the potential use of ML techniques and spectral response data to detect and estimate the concentration of oil-spills.