Microplastics can contaminate water owing to their small size. If aquatic biota consume microplastics, they disrupt their reproductive processes, digestive tracts, and development. This study aimed to identify microplastic waste from silkworms (Tubifex spp.) in the Brantas River. The study was conducted in a descriptive manner by collecting samples of microplastic waste from silkworms and examining the shape, type, amount, and percentage of microplastic abundance in the river. An FTIR test was used to determine the microplastic content. Using a Zeiss Axio Zoom.V16 at 50x magnification, microplastic particles from individual worms and worm samples were visually identified. Then, the 50% hot needle test was used to determine the composition of the plastic. A total of 263 microplastic particles were found in the worm samples. Silkworms (Tubifex spp.) in the Brantas River, Kediri City, were shown to contain four types of microplastics, namely fibers, filaments, fragments, and granules, which were dominated by filament particles with 49% filament content, 45% fiber, 5% fragments, and 1% granules. The microplastic polymers identified via FTIR were polyethylene and ethylene-polypropylene-diene copolymers. These microplastics can originate from plastic bags, used drinking bottles, rope fibers, and pieces of water hose, which are often found around the Brantas River. Silkworms found in the Brantas River contain microplastic waste from various pollution sources.

Chloroorganic chemicals emitted from the pulp and paper mill at Nekoski in central Finland were monitored for several years. Concentration time series are used for evaluating the environmental fate and the applicability and validity of an exposure models. Fitted elimination rates of 3,4,5-Tri-, 4,5,6-Tri-, Tetrachloroguaiacol and 2,4,6-Trichlorophenol are approx. 0.22 per day, or rather the half-lives are approx. 3 days. The elimination is most likely by biodegradation and transport-controlled. For 2,3,6-trichloro-p-cymene, fate simulations indicate significant volatilization and sedimentation. Good agreement is achieved with a one-dimensional steady-state box model, except for concentrations in fish. For a reliable assessment of environmental damage, laboratory experiments, monitoring and simulations need to be in tune.

The Black Sea receives increasing amounts of microplastics from rivers. In this study, we explore options to reduce future river export of microplastics to the Black Sea. We develop five scenarios with different reduction options and implement them to a Model to Assess River Inputs of pollutaNts to seA (MARINA-Global) for 107 sub-basins. Today, European rivers draining into the Black Sea export over half of the total microplastics. In 2050, Asian rivers draining into the sea will be responsible for 34–46% of microplastic pollution. Implemented advanced treatment will reduce point-source pollution. Reduced consumption or more collection of plastics will reduce 40% of microplastics in the sea by 2050. In the optimistic future, sea pollution is 84% lower than today when the abovementioned reduction options are combined. Reduction options affect the share of pollution sources. Our insights could support environmental policies for a zero pollution future of the Black Sea.

International audience | This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d’Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic. Finally, the ability of our original system RHIS to automatically recognize different types of plastic litter was assessed by applying different supervised machine learning methods on a set of representative image patches of marine litter. The obtained results highlighted the plastic litter classification capability with an overall accuracy close to 90%. This paper showed that the newly presented RHIS coupled with the UAD is a promising approach to identify plastic waste in aquatic environments.

In this paper the results of determination of natural waters from the territory of the township Smederevska Palanka and Rekovac (Serbia, Yugoslavia) are presented. The content of metals in waters was determined by the AAS method and other parameters were determined by spectrophotometric and potentiometric method.

Various physical, chemical and biological methods are used in complex investigations which are aimed at determining the quality of water. One of the oldest but also most widely accepted biological mathods is determination of saprobity on the basis of bioindicators. In application of these methods the existing lists of the indicators are most frequently used (Sladacek 1973, SEV 1977). In these lists only nine types of red algae (Rhodophyta) are specified as indicators of water quality. One of the main trends in development of algaeology in Serbia (Yugoslavia) is investigation of autoecological properties of algae and making a list of water quality indicator algae suited to the specific ecological conditions in water ecosystems of Serbia (Yugoslavia). The objective of this paper is to present information about apperance of red algae in the rivers of Serbia (Yugoslavia) and influence of physico-chemical factors upon their development, which represents a contribution to making of the above mentioned list of the water quality indicator algae.