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Cyanobacteria blooms are a worldwide concern for water bodies and may be promoted by eutrophication and climate change. The prediction of cyanobacterial blooms and identification of the main triggering factors are of paramount importance for water management. In this study, we analyzed a comprehensive dataset including ten-years measurements collected at Lake Varese, an eutrophic lake in Northern Italy. Microscopic analysis of the water samples was performed to characterize the community distribution and dynamics along the years. We observed that cyanobacteria represented a significant fraction of the phytoplankton community, up to 60% as biovolume, and a shift in the phytoplankton community distribution towards cyanobacteria dominance onwards 2010 was detected. The relationships between cyanobacteria biovolume, nutrients, and environmental parameters were investigated through simple and multiple linear regressions. We found that 14-days average air temperature together with total phosphorus may only partly explain the cyanobacteria biovolume variance at Lake Varese. However, weather forecasts can be used to predict an algal outbreak two weeks in advance and, eventually, to adopt management actions. The prediction of cyanobacteria algal blooms remains challenging and more frequent samplings, combined with the microscopy analysis and the metagenomics technique, would allow a more conclusive analysis.

Development of sensitive methods for the determination of E. coli bacteria contamination in water distribution systems is of paramount importance to ensure the microbial safety of drinking water. This work presents a new sensing platform enabling the fast detection of bacteria in field samples by using specific antibodies as the biorecognition element and dark field microscopy as the detection technique. The development of the sensing platform was performed using non-pathogenic bacteria, with the E. coli DH5α strain as the target, and Bacillus sp. 9727 as the negative control. The identification of the captured bacteria was made by analyzing the dark field microscopy images and screening the detected objects by using object circularity and size parameters. Specificity tests revealed the low unspecific attachment of either E. coli over human serum albumin antibodies (negative control for antibody specificity) and of Bacillus sp. over E. coli antibodies. The system performance was tested using field samples, collected from a wastewater treatment plant, and compared with two quantification techniques (i.e., Colilert-18 test and quantitative polymerase chain reaction (qPCR)). The results showed comparable quantification capability. Nevertheless, the present method has the advantage of being faster, is easily adaptable to in-field analysis, and can potentially be extended to the detection of other bacterial strains.

Toxic cyanobacterial blooms represent a natural phenomenon caused by a mass proliferation of photosynthetic prokaryotic microorganisms in water environments. Bloom events have been increasingly reported worldwide and their occurrence can pose serious threats to aquatic organisms and human health. In this study, we assessed the microbial composition, with a focus on Cyanobacteria, in Lake Varese, a eutrophic lake located in northern Italy. Water samples were collected and used for obtaining a 16S-based taxonomic profile and performing a shotgun sequencing analysis. The phyla found to exhibit the greatest relative abundance in the lake included Proteobacteria, Cyanobacteria, Actinobacteriota and Bacteroidota. In the epilimnion and at 2.5 × Secchi depth, Cyanobacteria were found to be more abundant compared to the low levels detected at greater depths. The blooms appear to be dominated mainly by the species Lyngbya robusta, and a specific functional profile was identified, suggesting that distinct metabolic processes characterized the bacterial population along the water column. Finally, analysis of the shotgun data also indicated the presence of a large and diverse phage population.

In the following article an electron/ion microscopy study will be presented which investigates the uptake of silver nanoparticles (AgNPs) by the marine diatom Thalassiosira pseudonana, a primary producer aquatic species. This organism has a characteristic silica exoskeleton that may represent a barrier for the uptake of some chemical pollutants, including nanoparticles (NPs), but that presents a technical challenge when attempting to use electron-microscopy (EM) methods to study NP uptake. Here we present a convenient method to detect the NPs interacting with the diatom cell. It is based on a fixation procedure involving critical point drying which, without prior slicing of the cell, allows its inspection using transmission electron microscopy. Employing a combination of electron and ion microscopy techniques to selectively cut the cell where the NPs were detected, we are able to demonstrate and visualize for the first time the presence of AgNPs inside the cell membrane.

The detection and quantification of nanoparticles is a complex issue due to the need to combine "classical" identification and quantification of the constituent material, with the accurate determination of the size of submicrometer objects, usually well below the optical diffraction limit. In this work, the authors show that one of the most used analytical methods for silver nanoparticles, asymmetric flow field-flow fractionation, can be strongly influenced by the presence of dissolved organic matter (such as alginate) and lead to potentially misleading results. The authors explain the anomalies in the separation process and show a very general way forward based on the combination of size separation and size measurement techniques. This combination of techniques results in more robust AF4-based methods for the sizing of silver nanoparticles in environmental conditions and could be generally applied to the sizing of nanoparticles in complex matrices.