Phycology

Biohydrogen is considered to be one of the fuels of the future, so there is a justified need to find efficient and cost-effective technologies for its production. This study evaluated the efficiency of two biohydrogen production pathways, specifically biophotolysis and dark fermentation, using Tetraselmis subcordiformis biomass. Microalgae production was performed in three variants, where the separation criterion was the type of culture medium: a control sample (synthetic medium; V1–PCR), agricultural wastewater from hydroponic tomato cultivation (V2–SL-WW), and effluent from a microbial fuel cell (V3–MFC-WW). The highest increase in biomass of T. subcordiformis was obtained in V2–SL-WW—2730 ± 212 mg VS/L, which was also associated with the maximum chlorophyll a content (65.0 ± 5.1 mg Chl-a/L). In biophotolysis, the highest specific hydrogen yields were obtained in V1–PCR (55.3 ± 4.3 mL/g VS) and V2 (54.3 ± 3.7 mL/g VS). The total hydrogen production in these variants was 166 ± 13 mL (V1–PCR) and 163 ± 11 mL (V2–SL-WW), respectively. The average H₂ production rate reached 4.70 ± 0.33 mL/h in V2–SL-WW, and the rate constant (k) was 0.030–0.031 h⁻¹. In anaerobic fermentation, the highest total and specific H₂ production was obtained in V1–PCR, 453 ± 31 mL and 45.3 ± 3.1 mL/g VS, respectively. The qualitative composition of the biogas confirmed a high hydrogen content: 61.4% (biophotolysis, V1) and 41.1% (dark fermentation, V2–SL-WW). The results obtained confirm that T. subcordiformis can be effectively cultivated on waste media and that the biohydrogen production maintains a high technological efficiency through both photolytic and fermentative mechanisms. The medium from hydroponic tomato cultivation (V2–SL-WW) proved to be particularly promising, as it combines high biomass productivity with a satisfactory biohydrogen production profile.

This study assesses the ecological conditions of the Colentina urban river system by investigating phytoplankton community traits, with a focus on resource use efficiency (RUE) as a functional indicator. Using phytoplankton biomass, taxonomic composition, and RUE, we assessed the ecological effects of anthropogenic pressures. Our results showed that total phosphorus values indicated chronic eutrophication conditions but supported increased phytoplankton biomass, especially in spring and summer. RUE varied independently of biomass, with maximum values recorded in autumn, suggesting a functional recovery phase, characterized by higher RUE under nutrient decline. The analysis at the phytoplankton group level highlighted distinct ecological strategies: cyanobacteria presented a high RUE in autumn, diatoms increased their efficiency during nutrient limitation periods, and green algae showed a functional flexibility throughout the study period. In contrast, spatial analyses indicated a decoupling between biomass and RUE, reflecting the influence of local environmental conditions on ecosystem functioning. RUE was significantly influenced by total phosphorus, nitrogen forms, temperature and light availability. Our results strengthen the combined approach of structural (biomass) and functional (RUE) indicators for the assessment of communities and anthropogenic impacts in urban and peri-urban aquatic ecosystems.

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health.