Selenium is essential for humans and the deficit of Se requires supplementation. In addition to traditional forms such as Se salts, amino acids, or selenium-enriched yeast supplements, next-generation selenium supplements, with lower risk for excess supplementation, are emerging. These are based on selenium forms with lower toxicity, higher bioavailability, and controlled release, such as zerovalent selenium nanoparticles (SeNPs) and selenized polysaccharides (SPs). This article aims to focus on the existing analytical systems for the next-generation Se dietary supplement, providing, at the same time, an overview of the analytical methods available for the traditional forms. The next-generation dietary supplements are evaluated in comparison with the conventional/traditional ones, as well as the analysis and speciation methods that are suitable to reveal which Se forms and species are present in a dietary supplement. Knowledge gaps and further research potential in this field are highlighted. The review indicates that the methods of analysis of next-generation selenium supplements should include a step related to chemical species separation. Such a step would allow a proper characterization of the selenium forms/species, including molecular mass/dimension, and substantiates the marketing claims related to the main advantages of these new selenium ingredients.

Artificial intelligence (AI) has become a useful tool in numerous domains, including environmental science. This review explores the application of machine learning and deep learning, as AI technologies, applied in calculating and modelling water quality indexes (WQIs) and water quality classification. WQIs are used to assess the overall status of water bodies and compliance with environmental regulations. Given a large amount of monitoring data, traditional methods for calculating WQIs can be labour-intensive and subject to human error. AI offers a compelling alternative, with the potential to enhance accuracy, reduce time, and provide insights into complex environmental data. This paper examines recent progress in applying AI to water quality assessment through WQIs, including the creation of predictive models that incorporate diverse water quality parameters and the implementation of AI in real-time monitoring systems. The challenges of deploying AI, such as data availability, model transparency, and system integration, are also discussed. Through a detailed analysis of recent studies and practical implementations, this review analyses the potential of AI to contribute to water quality management and suggests directions for future research.

Rapeseed meal (RSM), a by-product of oilseed extraction connected to the agri-food and biofuel sectors, is currently used as animal feed and for other low-value purposes. With a biorefinery approach, RSM could be valorized as a source of bio-based molecules for high-value applications. This study provides a chemical characterization of RSM in the perspective of its valorization. A qualitative study of main functional groups by fourier transform infrared (FTIR) spectroscopy was integrated with a chemical characterization of macronutrients, minerals by inductively coupled plasma optical emission spectrometry (ICP-OES), phenolic acids and lipid components by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), HPLC-diode-array detector (HPLC-DAD) and gas chromatography-mass spectrometry/flame ionization detector (GC-MS/FID). The study, conducted on different lots of RSM collected over a one-year period from an oil pressing factory serving a biofuel biorefinery, highlighted a constant quality over time of RSM, characterized by high protein (31–34%), fiber (33–40%) and mineral (5.5–6.8%) contents. Polyphenol extracts showed a significant antioxidant activity and a prevalence of sinapic acid, accounting for more than 85% of total phenolic acids (395–437 mg kg⁻¹ RSM). Results highlight the potentialities of RSM for further valorization strategies that may lead to the creation of new cross-sector interconnections and bio-based value chains with improvement of the economics and sustainability of the bioeconomy sectors involved.

The Danube River is the second longest in Europe and its water quality is important for the communities relying on it, but also for supporting biodiversity in the Danube Delta Biosphere Reserve, a site with high ecological value. This paper presents a methodology for assessing water quality and long-term trends based on water quality indices (WQI), calculated using the weighted arithmetic method, for 15 monitoring stations in the Lower Danube and Danube tributaries in Romania, based on annual means of 10 parameters for the period 1996–2017. A trend analysis is carried out to see how WQIs evolved during the studied period at each station. Principal component analysis (PCA) is applied on sub-indices to highlight which parameters have the highest contributions to WQI values, and to identify correlations between parameters. Factor analysis is used to highlight differences between locations. The results show that water quality has improved significantly at most stations during the studied period, but pollution is higher in some Romanian tributaries than in the Danube. The parameters with the highest contribution to WQI are ammonium and total phosphorus, suggesting the need to continue improving wastewater treatment in the studied area. The methodology and the results of the study may be very useful instruments for specialists and decision makers in updating river basin management plans and prioritising intervention measures.

Bucharest, the capital of Romania, is located on the banks of Dambovita River, tributary of Arges River, which, in its turn, flows into the Danube, the second longest river in Europe. Until 2011, Bucharest wastewater treatment plant (WWTP) had no advanced treatment, and since the end of 2011 the plant is able to treat half of the incoming flow. The second upgrading phase is under construction. This paper presents monitoring data of Dambovita River, upstream and downstream from Bucharest WWTP, during the period 2010-2015. Annual means of main nutrients concentrations show that water quality was mostly in the first class before the WWTP, according to Romanian norms, and in the worst class downstream from the WWTP, particularly for ammonium and total phosphorus, which are indicators of sewerage pollution. Pollution is attenuated by dilution after confluence with the Arges River. Principal Component Analysis and factor analysis of monitoring data show the differences between sampling locations and strong positive correlations between ammonium, orthophosphates and total phosphorus. Nutrient pollution downstream from Bucharest has decreased after 2010, but more efforts to improve wastewater treatment are needed in order to comply with national and international regulations.

The Danube River receives tributaries with different pollution loads, according to the social-economic characteristics of the adjacent regions. Water quality monitoring data from Chiciu, Calarasi county, Romania, for the three-year period (2010-2012), were analysed using statistical methods in order to identify correlations between parameters, as well as their evolution during the study period. The analysis has confirmedpositive correlations between nitrates and total nitrogen and between ortho-phosphates and total phosphorus. Negative correlations were found between water temperatures on one side and dissolved oxygen and nitrates on the other side. These parameters have a seasonal evolution, with high temperatures and low dissolved oxygen and nitrates levels during summer periods. Linear regression highlights decreasing nutrients pollution during the study period, which may be due to improved wastewater treatment along Danube tributaries.