The ambitious new International Maritime Organization (IMO) strategy to reduce greenhouse gas emissions from ships will shape the future path towards the decarbonization of the fleet and will bring further ecological challenges. In order to replace the larger oil-based part of marine fuel with components from renewable sources, it is necessary to develop multi-component blends. In this work, biomethanol and biodiesel with two additives—dodecanol and 2-ethylhexyl nitrate—in 20 blends with marine diesel oil (MDO) were selected as alternative components to replace the pure marine diesel oil-based part of marine fuel. For this purpose, two base blends of diesel and biodiesel with and without additives were produced with biomethanol from 0 to 30% (volume basis). Of all the blends, the blends with 5% (volume basis) methanol had the best property profile in terms of density, kinematic viscosity, calorific value, cloud point, and cetane index according to the ISO 8217:2017 standard (DMB grade) in compliance with the IMO requirements for marine fuels. However, the flash point must be increased. The boiling behavior of the blends was also investigated. A cluster analysis was used to evaluate the similarity between the blends based on their different physical properties.

This study presents a quantitative approach to mapping benthophagous fish feeding grounds. This approach combines the spatial biomass distribution of benthic prey items and their importance for the diets of predators. A point based biomass data of macrozoobenthos together with a set of environmental factors was used to develop Random Forests models that produce continuous biomass distribution layers for individual prey species. Depending on the diet composition and the importance of prey for fish feeding, these layers are overlaid and an integrated GIS map of the seabed showing the quality of feeding grounds is generated. These maps provide a useful basis for conservation and marine spatial planning. In addition, this method could be applied to the mapping of resources used by other benthophagous organisms. The method is presented using the example of three common Baltic fish species: cod, flounder and viviparous eelpout

The frequency of heatwave events in Europe is increasing as a result of climate change. This can have implications for the water quality and ecological functioning of aquatic systems. We deployed three spectroradiometer WISPstations at three sites in Europe (Italy, Estonia, and Lithuania/Russia) to measure chlorophyll-a at high frequency. A heatwave in July 2019 occurred with record daily maximum temperatures over 40 °C in parts of Europe. The effects of the resulting storm that ended the heatwave were more discernable than the heatwave itself. Following the storm, chlorophyll-a concentrations increased markedly in two of the lakes and remained high for the duration of the summer while at one site concentrations increased linearly. Heatwaves and subsequent storms appeared to play an important role in structuring the phenology of the primary producers, with wider implications for lake functioning. Chlorophyll-a peaked in early September, after which a wind event dissipated concentrations until calmer conditions returned. Synoptic coordinated high frequency monitoring needs to be advanced in Europe as part of water management policy and to improve knowledge on the implications of climate change. Lakes, as dynamic ecosystems with fast moving species-succession, provide a prism to observe the scale of future change.

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies.