And the winners are…

Many pharmaceuticals used daily by millions of people end up in the sewer. Because these molecules are small, they are difficult to filter out of sewage. The drug residues thus enter surface water, posing a threat to the water chain.

To address this problem, research was conducted to see if it is possible to develop toilet paper that will bond with the drug residues, so that they are better removed from sewage. Lignin, a common network polymer in plants, was found to bond well with the micropollutants. Our research showed that it is possible to significantly reduce the pollutant pharmaceuticals in surface water using toilet paper containing lignin.

LipoCleaning introduces the issue of water pollution in the textile industry, which is one of the industrial sectors that utilize and pollute the most considerable amounts of water. We intend to treat water before using it for dyeing processes, by adding something natural, accessible economically and, most importantly, that doesn’t end up polluting in turn, which would mean going against our main objective: recycling water. In order to meet our requirements we decided to optimize the process by adding soy lecithin which works as a vehicle for the dye. At the end of the process, we were able to, thanks to the separation of lecithin from the dyebath, obtain clean water that can then be re-inserted into the industrial process.

Today, in any field of science, we can observe interdisciplinary directions, which are born from the fusion of related disciplines. By combining scientific fields and at the same time comparing the methods of different fields, we can get answers to new questions that go beyond a single subject. Understanding the niche model of ecology gives a new dimension to the complex study of the composition of living communities. The habitat of each population is determined by biotic and abiotic factors. The examination of biotic variables is the task of ecology, while abiotic variables cannot be examined with ecological methods, as the scales often used for their evaluation are too robust for detailed analyses. The measurements carried out by hydrologists and hydraulic engineers can provide a much more accurate description of these abiotic variables, so by combining the two, we can discover new relationships. In this study, we assigned the data of the 20 most common fish species in the Hungarian section of the Danube River from 2004 to 2022 to the data of hydrological datasets and hydrodynamic simulation models, and looked for patterns among them using Machine Learning (ML). Among the nine abiotic factors used as independent variables in the analysis, the average depth velocity, water depth and bed material composition were the most decisive variables, which aligns with the results of previous research. In addition, with our Random Forest model, we were able to predict the number of individuals of the 20 most common fish species in the given conditions in the entire Hungarian section of the Danube. These estimates refer to optimal habitat for fish species according to abiotic variables. The model gives accurate values only in a narrow range, the so-called hydromorphological optimum, where our variables determine the abundance of fish. The results of the studies showed that in most cases biotic factors are more dominant than abiotic variables. In addition to the ML analysis, we showed the possibility of using the Danube fish faunistic database, which covers a large area and time, to investigate the relationships of the population (for example, the relationship between invasive and native species) using classical statistical methods. The results found here are in many cases consistent with the Random Forest model, but give reason to extend the model with additional independent variables in order to better understand the ecology of the Danube fish species.

.

Water safety has become a paramount concern due to various issues such as pollution, leakages, and obstruction in distribution pipelines.

This research project was undertaken to develop a scalable technology-driven system for smart houses that can detect, predict and report water quality, leakage & clogging issues.

By predicting leakages issues in advance, the system has the potential to avoid seepage and mould problems which cause serious health risks.

The system leverages sensors, IoT, and AI technology. It collects and analyses the data from the sensors, and generates alarms in real-time if any water-related issues are indicated. Alarms can be reported via SMS or displayed on a dashboard. Water Security Index is calculated to assess the safety of the water supply within the premises in real time. 

The system can be scaled up for larger areas such as buildings, residential societies, and cities using LoRa network technology.

The presence of microplastics in freshwater is a pressing environmental issue, and effective detection methods are crucial for addressing this problem. While techniques like spectrophotometry and chelating agents have been used to detect microplastics, the use of sound as a detection method remains unexplored. This study employed a novel approach by using river water samples from different locations in Pamplona and using a speaker, tuner, and laboratory instruments to develop a simple and practical procedure for detecting microplastics in the water. Rigorous controls were implemented to ensure accurate measurement of plastic and minimize interference from external factors.

Sur terre, les eaux des mers et des océans occupent environ 71% de la planète (selon www.pure-ocean.com ). Pourtant son utilisation est impossible dans plusieurs domaines, hormis son utilisation pour la fabrication du sel, du fait de sa forte concentration en sel (environ 35g/L).

Plusieurs études et recherches ont néanmoins conduit à la découverte de méthodes permettant de réduire ou d’éliminer cette quantité de sel présente dans ces eaux. Mais ces techniques n’apportent pas leur accessibilité à tous en raison de leurs coûts très élevés.

C’est la raison pour laquelle nos recherches ont abouti à la mise en place de ce projet qui s’intitule le dessalement de l’eau de mer à l’aide d’une solution que nous avions appelée « solution E »

Notre méthodologie permet de déclencher la formation de flocons de sel (phénomène de floculation) et de faciliter le phénomène de décantation.

Le résultat obtenu à la fin de l’expérience est une eau dont la concentration en sel est inférieure de plus de 80 % à celle contenue dans l’eau de mer à l’état naturel.

Microplastic pollution poses a global threat to marine life. Effective and eco-friendly solutions for removing microplastics(MPs) from water are urgently required. This study aims to capture microplastics with magnetic nano-adsorbents and remove them from the water using strong magnets. Magnetic carbon nanotubes (M-CNTs) were synthesized as nano-adsorbents because they can successfully adhere to the surfaces of MPs due to their strong hydrophobicity. The study tested various experimental parameters that affect the adsorption of MPs by M-CNTs, including the amount of adsorbent, contact time, salinity of water, and MP polymer type. The results showed that the water was cleansed of microplastics with an average success rate of ≈98%. Furthermore, for real-life applications, a magnetic water treatment machine using robotics was built.

The agricultural sector uses 70% of water withdrawals worldwide and uses nutrients that are leached from the soils into lakes and reservoirs. Vertical farming has great potential to remedy some of these issues. Vertical farming, where crops are grown vertically indoors, uses more than 600% energy per kg of crops produced compared to traditional greenhouses. By reducing the amount of light exposed to the lettuce plant rocket, a reduction of this energy consumption can be achieved. By exposing rocket to 7 hours of light per day an energy saving of approximately 10% per kg of crops is achieved (compared to the average vertical farm). This saving makes vertical farming a more desirable supplement for traditional agriculture, thus reducing water use.

Research project “Portable device for water purification in emergencies” is dedicated to the issue of ensuring access to safe drinking water in emergency situations. In the conditions of war, the quality of drinking water becomes extremely important. Completely or partially destroyed water infrastructure facilities, soil and water resources contaminated with hazardous substances due to numerous rocket attacks and damaged industrial facilities – all this deprives people of access to safe drinking water. Young researchers has developed a portable water treatment device that, unlike mobile complex surface or groundwater treatment systems, does not require an uninterrupted power supply. The use of such a device with a “carbon block” type filter element in extreme conditions allows to ensure the basic needs of the people in safe drinking water.

This project aims to investigate the potential use of thermally treated seagrass P. Oceanica to recover phosphate from wastewater and the production of a fertilizer substitute. Posidonia Oceanica residues were thermally treated at 550o C for 15 minutes and were tested as an adsorbent of phosphate for water collected from Finikaria dam. Results shows that thermally treated seagrass could be applied as adsorbents for Phosphate from synthetic and waster from dam (high in phosphorous), with 98% and 97% adsorption efficiency, respectively. Finally, by using thermally treated P. Oceanica, which was exposed to phosphate, as a potential fertilizer to lens it seems to positively affect their growth.

The project goal was the development of a sustainable alternative to textile effluent treatment, through biomembranes produced with wine by-products. The biomembranes were produced in a static fermentation for 14 days. The biomembranes were tested as adsorbent material to remove the indigo carmine dye in three different concentrations (5 mg/L, 15 mg/L, and 50 mg/L). Removal of 50%, 80%, and 80%, respectively, was observed in the three concentrations. Two industrial textile effluents were tested: green and red. The alternative treatment removed 50% of green effluent and 30% of red effluent. This project has social, environmental, and scientific relevance in developing a sustainable alternative for textile effluent treatment, addressing 8 of the 17 SDSs.