Conventional plastics are inherently difficult to degrade, causing serious plastic pollution. With the development of society, biodegradable plastics (BPs) are considered as an alternative to traditional plastics. However, current research indicated that BPs do not undergo complete degradation in natural environments. Instead, they may convert into biodegradable microplastics (BMPs) at an accelerated rate, thereby posing a significant threat to environment. In this paper, the definition, application, distribution, degradation behaviors, bioaccumulation and biomagnification of BPs were reviewed. And the impacts of BMPs on soil and marine ecosystems, in terms of physicochemical property, nutrient cycling, microorganisms, plants and animals were comprehensively summarized. The effects of combined exposure of BMPs with other pollutants, and the mechanism of ecotoxicity induced by BMPs were also addressed. It was found that BMPs reduced pH, increased DOC content, and disrupted the nitrification of nitrogen cycle in soil ecosystem. The shoot dry weight, pod number and root growth of soil plants, and reproduction and body length of soil animals were inhibited by BMPs. Furthermore, the growth of marine plants, and locomotion, body length and survival of marine animals were suppressed by BMPs. Additionally, the ecotoxicity of combined exposure of BMPs with other pollutants has not been uniformly concluded. Exposure to BMPs induced several types of toxicity, including neurotoxicity, gastrointestinal toxicity, reproductive toxicity, immunotoxicity and genotoxicity. The future calls for heightened attention towards the regulation of the degradation of BPs in the environment, and pursuit of interventions aimed at mitigating their ecotoxicity and potential health risks to human.

Surging dismissal of plastics into water resources results in the splintered debris generating microscopic particles called microplastics. The reduced size of microplastic makes it easier for intake by aquatic organisms resulting in amassing of noxious wastes, thereby disturbing their physiological functions. Microplastics are abundantly available and exhibit high propensity for interrelating with the ecosystem thereby disrupting the biogenic flora and fauna. About 71% of the earth surface is occupied by oceans, which holds 97% of the earth\'s water. The remaining 3% is present as water in ponds, streams, glaciers, ice caps, and as water vapor in the atmosphere. Microplastics can accumulate harmful pollutants from the surroundings thereby acting as transport vectors; and simultaneously can leach out chemicals (additives). Plastics in marine undergo splintering and shriveling to form micro/nanoparticles owing to the mechanical and photochemical processes accelerated by waves and sunlight, respectively. Microplastics differ in color and density, considering the type of polymers, and are generally classified according to their origins, i.e., primary and secondary. About 54.5% of microplastics floating in the ocean are polyethylene, and 16.5% are polypropylene, and the rest includes polyvinyl chloride, polystyrene, polyester, and polyamides. Polyethylene and polypropylene due to its lower density in comparison with marine water floats and affect the oceanic surfaces while materials having higher density sink affecting seafloor. The effects of plastic debris in the water and aquatic systems from various literature and on how COVID-19 has become a reason for microplastic pollution are reviewed in this paper.

The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy.

BACKGROUND: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers\' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers\' performance.
METHODS: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities.
RESULTS: The developed methodology is tested on the maintenance of marine engine\'s cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared.
CONCLUSIONS: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.