Oomycete pathogens in freshwaters, such as Saprolegnia parasitica and Aphanomyces astaci, are responsible for fish/crayfish population declines in the wild and disease outbreaks in aquaculture. Although the formation of infectious zoospores in the laboratory can be triggered by washing their mycelium with natural water samples, the physico-chemical properties of the water that might promote sporulation are still unexplored. We washed the mycelia of A. astaci and S. parasitica with a range of natural water samples and observed differences in sporulation efficiency. The results of Partial Least Squares Regression (PLS-R) multivariate analysis showed that SAC (spectral absorption coefficient measured at 254 nm), DOC (dissolved organic carbon), ammonium-N and fluoride had the strongest positive effect on sporulation of S. parasitica, while sporulation of A. astaci was not significantly correlated with any of the analyzed parameters. In agreement with this, the addition of environmentally relevant concentrations of humic acid, an important contributor to SAC and DOC, to the water induced sporulation of S. parasitica but not of A. astaci. Overall, our results point to the differences in ecological requirements of these pathogens, but also present a starting point for optimizing laboratory protocols for the induction of sporulation.

Bromate formation is a complex process that depends on the properties of water and the ozone used. Due to fluctuations in quality, surface waters require major adjustments to the treatment process. In this work, we investigated how the time of year, ozone dose and duration, and ammonium affect bromides, bromates, absorbance at 254 nm (UV254), near-infrared (NIR) spectra, and fluorescent components (humic-like and tyrosine-like) during surface water ozonation. Linear and non-linear models were used to determine and predict the relationships between input and output variables. Season, ozonation dose and time were correlated with the output variables, while ammonium affected only bromates. All coefficients of determination (R²) for the multiple linear regression models were >0.64, while R² for the piecewise linear regression models was >0.89. The season had no effect on bromate formation in either model, while ammonium only affected bromides and bromates. Three input variables influenced UV254 in both models. The artificial neural network (ANN) model with the season, ozonation dose and time, ammonium, and NIR spectra was an effective way to describe water ozonation results. The multilayer perception neural network 14-14-5 had the lowest errors and was the best ANN model with R² values for training, testing, and validation of 0.9916, 0.9826, and 0.9732, respectively.

The stone crayfish, Austropotamobius torrentium, is a native European freshwater species sensitive to habitat alterations such as stream channelization and urban development, putting its populations at risk. This study aims to assess how habitat alteration and subsequent water quality changes affect the stone crayfish population in the Kustošak Stream (Croatia) through three selected sites under different levels of anthropogenic disturbance: (1) undisturbed; (2) recently modified, and (3) long-time modified sites. At each site, crayfish were captured, measured, and marked to estimate population size, structure, and crayfish condition. Additionally, we examined whether water quality (18 measured physicochemical parameters) affects relative crayfish abundance. We also used mitochondrial sequencing and microsatellite genotyping to assess species genetic diversity and population connectivity and to compare results among the sites. The results showed that habitat alteration caused an overall reduction in population abundance and changes in size structure; however, it had not yet resulted in detectable genetic differences. Partial least squares regression showed that crayfish abundance was affected by the physicochemical parameters of water, including, among others, oxygen, calcium ions, nitrates, pH, and water temperature. We discuss our findings in the context of the effects of anthropogenic disturbance on the viability and persistence of this EU priority species.

This study focuses on optimizing the ozonation process in drinking water production from Lake Butoniga to ensure safe water quality while minimizing disinfection by-products (DBPs). Laboratory simulations were conducted using the Box–Behnken design to model the effects of ozone dose and treatment duration on bromate formation, trihalomethanes (THMs), haloacetic acids (HAAs) and specific UV absorption (SUVA). Two ozonation strategies were tested: Strategy 1 aimed to minimize all DBPs, while Strategy 2 focused on controlling bromate levels while keeping THMs, HAAs and SUVA below 80% of maximum contaminant levels. Results showed that Strategy 2 reduced ozone consumption while maintaining water quality within regulatory standards, providing a cost-effective and environmentally sustainable treatment approach. Seasonal and depth-dependent variations in water quality had a significant impact on treatment efficiency and required adjustments to operational settings. The study also addressed discrepancies between laboratory and real plant results and suggested recalibration methods that improved the accuracy of model predictions. These results highlight the potential for integrating predictive modelling and dynamic treatment strategies into large-scale water treatment processes.