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.

The research mainly studies the impact of nitrate-contaminated groundwater on B. subtilis growth and its influence on plant growth. Ion chromatography analysis of nitrate-contaminated groundwater samples reveals varying media compositions that sustain B. subtilis growth. Nitrate uptake efficiency is evaluated through ion chromatography, indicating a reduction in nitrate content as microbial growth progresses.

Furthermore, the application of biologically treated groundwater by B. subtilis promotes plant growth, exemplified by kidney beans. The study’s significance lies in securing water resources and ensuring sustainable food production. Future research will examine the long-term effects of microorganisms on groundwater, encompassing crop productivity, water conservation, and ecosystem considerations.

On land, the waters of the seas and oceans occupy about 71% of the planet (according to www.pure-ocean.com). However, its use is impossible in several regions, except for the production of salt, because of its high salt concentration (about 35g/L).
However, several studies and research have uncovered ways to reduce or eliminate this amount of salt in these waters. But these techniques are not accessible to everyone because of their very high costs.
This allowed us to focus our study on the desalination of seawater with the wood center
Our methodology allows to trigger the formation of salt flakes (phenomenon of flocculation) and to facilitate the phenomenon of settling.
At the end of the experiment, the salt concentration is reduced by more than 80%.

My project involves research into the causes of toxic cyanobacteria blooms and the development of a system which mitigates the risk. Cyanobacteria blooms plague global waterways and seriously impact the health of aquatic organisms, terrestrial animals, and humans. This threat is growing in prevalence alongside climate change and thus I aimed to design a system which prevents uncontrolled cyanobacteria reproduction (a bloom event), whilst allowing natural populations to exist.

The design, the SolarCyanoSlayer, is also aimed to be self-sustaining for long-term implementation and constructed of biologically derived and recycled materials, reducing the embodied energy of construction. When placed in simulated eutrophic conditions the invention was demonstrated to be highly effective in countering cyanobacteria blooms and subsequently improving overall water quality.

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.

This project addresses simultaneous removal of ocean carbon removal and soluble oil-in-water contaminants through the creation of a Multi-Functional Remediation Framework (MF-RF), using collected Styrofoam ocean debris to, in turn, remove carbon contaminant.

Our project is nested in the water-food-energy nexus. Water quality monitoring is crucial, especially in communities dealing with water scarcity. However, current state-of-the-art systems are expensive and complex, thus unsuitable for developing countries and enthusiasts. Our innovation offers a modular, accessible, and affordable solution.
We developed a water control and monitoring unit combining the ESP-32 and Raspberry Pi microcontrollers with cloud-based databases, using well-established frameworks and industry-standard MQTT IoT protocols to ensure reliable communications and management of sensors. The system is intuitive for end-users and can be easily customized by adding sensors and switches. By using free, open-source software and custom hardware, we significantly reduced costs, making water monitoring more attainable and sustainable for communities passionate about environmental stewardship.

Traditional methods for quality testing of water bodies are often slow and expensive, which limits the frequency at which these tests can be performed, often leading to late detection or complete oversight of water pollution. This delay can result in severe environmental damage and costly remediation efforts.

This research introduces an autonomous system that uses a drone to collect water samples from multiple water bodies within a 10km radius at conditionally predetermined intervals and transport them to portable analytical labs for frequent, cost-effective, and autonomous water testing, and analysis.

This project specifically addresses the development of an autonomous analytical lab, where tests are conducted to analyze four indicators which characterize water quality. If the results exceed the specified regulatory threshold level (RTL) set by the user, an alert is activated.

The study explores a process that conducts quick statistical analysis of the four indicators to assess the probability of pollution of the water body. If pollution is detected, the system recommends conducting further tests in a traditional lab to determine the specific nature of the contamination.

Kandelia obovata, one of the mangrove species, provides a low-cost solution for recovering heavy metals and improving water quality, which could be useful, especially in developing countries with limited wastewater treatment. In this research, we found that heavy metals such as arsenic, cadmium, and lead accumulate in the fallen leaves of Kandelia obovata.

Fallen leaves contain six times more cadmium than young leaves. By collecting these fallen leaves, along withother toxic heavy metals, about 2.01 kg of arsenic and 1.29 kg of cadmium can be recovered per 1 km2 of yellow mangrove forest per year. This method would provide a practical means of addressing heavy metal pollution in water bodies.

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.