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This research presents a next-generation AI-powered rapid water testing system designed for real-time, high-precision water quality monitoring in both industrial and domestic settings. At its core is the ESP32-C6 microcontroller, integrated with a 1.47-inch TFT display for immediate, on-site data visualization. The device utilizes five complementary analytical techniques; capacitance measurement, resistance analysis, ultraviolet (UV) exposure, infrared (IR) absorption, and Raman spectroscopy to evaluate water quality within seconds. These multi-modal sensing approaches enable the system to detect a wide spectrum of contaminants. Capacitance and resistance measurements help identify inorganic ions, dissolved salts, and microbial presence. UV and IR absorption offer rapid insights into organic pollutants, while Raman spectroscopy provides detailed molecular fingerprinting. Collectively, these techniques allow the estimation of key indicators such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), total coliforms, and fecal coliforms. The system can also identify heavy metals, synthetic dyes, microplastics, and pathogens like E. coli in real time. An embedded AI model, trained on a large and diverse dataset of water samples, interprets the data to detect complex contamination patterns with high reliability. This results in a portable, energy-efficient, and cost-effective solution capable of delivering immediate water quality assessments, empowering users to make proactive, informed decisions regarding water safety. However, the system does have certain limitations. It cannot measure all possible water quality parameters such as specific pesticide residues with high accuracy due to the constraints of sensor selectivity and hardware resolution. Moreover, while the device performs well in a broad range of scenarios, it may require calibration or additional modules to ensure accuracy across highly specialized or extreme conditions.

The project aims to develop a system that detects water loss using sound signal processing algorithms via a single sensor placed in a commonly used sink area and enables prompt intervention by a responsible staff member.  The developed water sound recognition algorithm was tested in different sinks; the experiments demonstrated that it achieved an accuracy rate of 77.3% and could prevent 15.25 liters of water waste in 135 minutes in a single restroom. Additionally, we trained an AI model to classify the sound, and our AI algorithm achieved a classification accuracy of 95%. Using a contactless approach, this system provides a low-cost, fast-deploying, and actionable solution that ensures water conservation and sustainability.

My project focused on detecting heavy metal pollution in rivers using fish scales as an environmentally friendly sampling material. I collected chub scales from sites along the Jihlava and Oslava Rivers, both upstream and downstream of wastewater and mine water treatment plants. The scales were cleaned, digested, and analyzed using ICP-MS. I measured concentrations of lead, copper, and cadmium, and assessed how they vary with fish size and sampling location. The findings suggest that treatment plants may contribute to pollution. Fish scales proved to be a sensitive and non-lethal tool for monitoring heavy metal contamination in aquatic ecosystems.

Climate change, a major challenge of the 21st century, profoundly affects societies and biodiversity. In the Swiss Alps, it causes glacier melting, intensifies natural hazards, and threatens the local economy dependent on winter tourism. This study analyzes its impact in the Fribourg Prealps, specifically on the Tissiniva alpine pasture, where water resources are particularly fragile. The reduction in snowfall, earlier snowmelt, and periods of drought complicate water supply, forcing the alpine pasture to adapt. By combining field observations, long-term meteorological data, and local testimonies, this research aims to better understand climate evolution in mountain regions and its environmental, economic, and social consequences.

Many cities suffer the consequences of flooding after a large amount of water
falls in a short period of time. Therefore, this project determines how urban
vegetation modifies soil structure, improving its water infiltration capacity and
reducing flood damage, putting Nature-Based Solutions (NBS) into practice.

A humidity sensor was built using Arduino and the humidity and water
infiltration capacity of soils influenced by five tree species and seven different shrub
species were measured. Analysis of the results obtained shows that areas
occupied by shrubs infiltrate a greater amount of water. In this sense, species such
as rosemary (Salvia rosmarinus) and oleander (Nerium oleander) stand out for the
development of their root system.

With this information, an image recognition model was created and trained
using LearningML, capable of identifying the studied plants in situ and indicating the
infiltration capacity of the surrounding soil. From it, a Geographic Information
System (GIS) was generated using the QGIS program, which shows the areas of our
city most vulnerable to torrential rains and flooding.

Our project aims to develop an efficient, effective, and eco-friendly method for removing microplastics from water. Under ultrasound, we allow commercially available magnetic nano-γ-Fe2O3 to stick onto microplastics in water. Once adsorbed with magnetic nano-γ-Fe2O3, microplastics become magnetic and can be removed easily with a magnet. 

Our method achieves up to 99% efficiency for common plastics (PE, PS, PP, PET) in just 20 minutes. It is cost-effective, avoids secondary pollution, and works across different plastic types and sizes. 

This study developed a sustainable system to treat and reuse nejayote, the potentially polluting wastewater from maize nixtamalization—a key step in making tortillas, a staple food and cultural icon in Mexico. Using a mechanical sedimentation process that requires no electricity, solids are separated and the liquid is clarified within 90 minutes, allowing water to be reused up to three times and reducing overall consumption. The recovered solids can be reincorporated into the dough, enhancing tortilla quality. An environmental label is also proposed to identify sustainable producers. Experimentally validated, the system is technically, economically, and environmentally viable. Aligned with the 2030 Agenda, this replicable model promotes circular practices, supports resource recovery, and offers environmental and economic benefits at both local and global levels.

The project offers an innovative solution for lobster fishing by utilizing mesquite wood, an invasive species prevalent in the semi-arid region of northeastern Brazil. Unlike polluting materials like tires, barrels, and contaminated items that capture juvenile lobsters, the proposed device is biodegradable, eco-friendly, and respects the species’ reproductive cycle. After 6 to 10 months of use, it transforms into a substrate for coral reefs and contributes to the fertilization of marine soil. Interviews with fishermen, technical evaluations, and validations from environmental agencies have already been conducted, confirming its feasibility and local acceptance. The initiative combats overfishing, preserves marine ecosystems, and values artisanal fishing. With global potential, the project aims to shift fishing practices by uniting innovation, sustainability, and marine regeneration.

Water faces serious threats, and leaks drive the need for innovative solutions. In Anabta, 40% of water is lost due to leakage (Anabta Municipality, 2021), including a 20-year-old undetected leak that caused street collapse. In the West Bank, non-revenue water reached 62% in Anata due to illegal connections, and 51–55% in other areas like Jenin and Bal’a due to leaks. In Gaza, losses reached 58% in Al-Maghraqa. Globally, Bangalore and Tokyo suffer significant leak-related losses.

Our robot, EDM, uses Arduino WeMos D1 with sensors along pipelines and a Raspberry Pi mobile robot with a camera and servo arm to detect and remove invasive roots. Data is sent via network to authorities, and leaks trigger automatic pump shutdown—advancing SDG 6 and SDG 11 in Palestine.

A possible solution to the increasingly urgent protein deficiency could be green biorefining, which is a process to get edible proteins out of plants. But one of the produced by-products called brown juice is harmful to the environment in large quantities, for example, it can cause eutrophication. In my research we wanted to get advantage of this situation by using the brown juice as an additive in the growing media of microalgae to increase the production and the nutritional value of the harvested biomass. Recycling brown juice this way helps to fight with protein deficiency because the harvested biomass contains high amount of protein, suitable for human or animal consumption.

paPECo is an innovative system designed to treat phenolic compounds in industrial wastewater using photoelectrocatalysis. It works by catalyzing a semiconductor immobilized on an electrode with light and an electrical potential. The optimized FTO/WO₃/BiVO₄ electrode induces a continuous oxidation reaction, effectively decomposing phenolic compounds into carbon dioxide. This system offers rapid treatment within one hour, operates at a low energy consumption of 2.5 V, and reduces CO₂ emissions. In the context of fossil fuel-based electricity, paPECo promotes sustainable economic growth and while ensuring environmental responsibility, especially in water management. It aligns with SDG 6, 13, and 14, supporting global sustainability efforts.

This research explores using natural sulphurous waters for various chemical applications. It focuses on precipitating heavy metal ions, eliminating free radicals, and recovering sulphur.

Key findings include successful H₂S detection, and sulphur recovery through oxidation with hydrogen peroxide. H₂S effectively precipitated several heavy metals (except chromium) and neutralized free radicals. The study demonstrates that sulphurous water is valuable for chemical reductions, metal precipitation, organic synthesis, and radical scavenging, suggesting it could be a sustainable source for sulphur recovery, reducing reliance on less healthy extraction methods.

In our project, we present a novel method for detecting volatile organic compounds (VOCs) in water using a Needle Trap Device (NTD) filled with a recently developed metal-organic framework sorbent (MOF), ZnMOF-74. VOCs, even at low concentrations, pose serious health and environmental risks. Omnipresent in rivers, near factories, and in tap water, exposure may lead to liver complications, lung disease, and cancer. The ZnMOF-74 sorbent demonstrated high reusability, sensitivity, and reproducibility in capturing VOCs such as isoprene, acetone, benzene, and p-xylene. Compared to conventional methods, this approach offers improved detection limits and environmental compatibility. The findings highlight the potential of MOFs as sorbent materials for the extraction of harmful chemicals and contribute to creating a safer aquatic environment.

Severe water scarcity in off-grid desert-edge and coastal communities requires desalination methods and contamination removal that are independent of energy-intensive such as reverse osmoses methods that requires about 3 to 5.5 kWh per cubic meter of water. This solution presents a cost-effective, solar-powered system designed to remove a wide range of contaminants then desalinate it by distillation. synthetic zeolite A, synthesized from kaolinite, adsorbs heavy metals and minimizes the chemical oxygen demand (COD). Concurrently, an osmotic-pressure-driven hydrogel formed from poly sodium acrylate, methylenebisacrylamide, ammonium persulfate for absorbing water and making salt rejection, and polyaniline for enhancing light absorption incorporated in a loofah sponge.

Waver is an innovative aquatic drone designed to restore and monitor dead zones in aquatic environments by generating artificial waves and collecting real-time environmental data. Equipped with smart sensors, solar panels, and GPS, Waver operates autonomously while monitoring key parameters such as temperature, pH, and oxygen levels. It integrates three AI models specialized in detecting plastic waste, oil leaks, and marine life, supported by a robust dataset for high accuracy. A user-friendly online platform displays live data and alerts, enabling informed decision-making for environmental management. Waver aims to support biodiversity recovery, promote sustainability, and offer a scalable solution to one of the most pressing ecological issues aquatic ecosystem degradation.

Since February 24, 2022, Ukraine has been experiencing large-scale military operations that have led to the destruction of infrastructure and significant environmental pollution, including water bodies, with toxic compounds, especially heavy metals. Heavy metals are capable of bioaccumulation, do not dissolve or decompose in water. Pollution with them affects the physiological and morphological parameters of fish, which worsens their quality as a food product and poses a threat to public health. The purpose of the study was to investigate the long-term effect of copper at a background concentration on the protective mechanisms of fish

As an innovative approach to reducing heavy metal pollution, we propose the creation of a constructed wetland, an engineered shallow coastal zone that combines natural and artificial components to improve the filtration and accumulation of heavy metals.

In this scientific research work, the hypothesis was tested, whether it is possible
to obtain drinking water for human consumption by filtering the freshwater through
natural materials. As part of the research, experiments were carried out in which the
water sample was filtered through sand, charcoal, and moss. After filtration, the water
was boiled at 100 °C for 10 minutes. After boiling, the water samples were tested in the
Scientific Institute of Food Safety, Animal Health and Environment BIOR (Latvia, Riga). By
comparing the obtained test results, it was concluded that these filter materials,
combined with boiling, have great potential to filter drinking water for short-term use.

This research project presents the development of a low-cost solar water distillation system designed to improve access to clean drinking water in off-grid and rural communities. This project explores the use of a Fresnel lens to focus sunlight onto a metal boiling pot. The resulting steam passes through food-grade silicone tubing and condenses into clean water, with no reliance on electricity – which means that there is no extra carbon emissions due to this. Three experiments were conducted where the independent variables included initial water volume, light intensity and natural weather conditions. The system produced consistent yields even in moderate climates like the UK.

AquaWise is a smart, automated irrigation system designed to optimize water use through customizable schedules – start/stop times, duration, flow rate, and total volume. It connects to the irrigation network and adjusts water distribution based on real-time data and plant’s production. The system integrates weather forecasts, environmental conditions (humidity, rainfall, wind, temperature, light), and local soil moisture sensor input to improve efficiency and reduce water waste. AquaWise is scalable for use in gardens, farms, or large agricultural setups and it includes machine learning. It requires basic hardware: affordable soil moisture sensors and a Raspberry Pi programmed in C++. The system controls a hydraulic valve and, if required, a pump. A pilot version is currently implemented in a school garden.  

In view of the increasing number of heavy rainfall events, we have developed WarnMe – a flood warning system. It records water levels in real time using infrared and ultrasound sensors and measures flow velocity via radar. The data is sent to our server via LoRaWAN, where it is analyzed using intelligent algorithms. A user-friendly app ensures that users are immediately informed of potential hazards. Simple installation instructions enable independent setup without professional assistance, creating a decentralized, cost-effective, and scalable system. In summer 2024, WarnMe successfully detected an imminent flood event and contributed to public safety by issuing timely warnings to the affected population.

We created a compact, low-energy system that treats and makes kitchen greywater safe to reuse by combining electrocoagulation, UV light, and multi-stage filtration. It fits under the sink and automatically operates with an Arduino Mega board, reducing water use by up to 83%. In our hometown, Belén, Catamarca — a region affected by water scarcity and poor water quality — kitchens consume around one billion liters of water each year. Our system could recover up to 900 million liters annually, offering a sustainable and affordable way to improve water use, reduce the demand for clean drinking water, and minimize pollution.

Oil spills devastate marine ecosystems, and conventional cleanup methods are often dangerous and inefficient. Our project, the Oil Recovery and Cleanup Autonomous (ORCA) system, presents a safer, smarter solution. This fully autonomous prototype integrates AI for oil detection (97% precision), LiDAR-based navigation for obstacle avoidance, and an optimized oleophilic surface for collection. Rigorous testing confirmed high skimming efficiencies up to 75.7% and excellent water rejection of 96%. ORCA demonstrates a viable, next-generation approach to oil spill remediation, capable of operating effectively without risking human lives, protecting both our oceans and the people who work to save them.

As water scarcity worsens due to climate change, atmospheric water harvesting is emerging as a sustainable solution for freshwater supply in regions with limited access. This study presents an energy-efficient atmospheric water harvesting device based on metal–organic frameworks (MOFs). Unlike existing systems that operate only once daily using temperature differences between day and night or require electricity, the proposed device collects water multiple times daily without external power. Integrating MOFs with manual vacuum pump and low-temperature sources like soil accelerated water collection up to fourfold compared to non-vacuum conditions. Incorporating MOFs into a porous sponge shaped into beads or films enhanced water uptake efficiency and portability. This approach offers a promising strategy for freshwater production across diverse climate conditions.

Ameland is an island with unique challenges and opportunities in drinking water production. An increasing demand for drinking water due to growing tourism and a growing permanent population creates challenges. The current drinking water supply largely relies on freshwater bubbles under the island and water brought in from the mainland via pipes. However, this approach is fragile and not fully sustainable. Therefore, this paper investigated whether electrodialysis metathesis (EDM) could contribute to a self-sufficient and future-proof drinking water system for Ameland. The study concludes that EDM is a promising technique for producing drinking water from seawater on Ameland. The road to a self-sufficient drinking water system is still long and has many obstacles, but this research shows that with the right innovations and knowledge, it is possible.

Climate change often evokes flooding, forest fires, or melting ice caps. However, one invisible but significant impact is increased evaporation from large lakes, impacting recreation, fishing, irrigation, and hydropower. 97% of Manitoba’s electrical production comes from hydropower dams, which is threatened by increased evaporative losses from reservoirs due to rising temperatures. While evaporative losses currently account for 15-20% of annual storage, inadequate weather data hinders accurate estimates. Deep Neural Networks offer a solution to this gap, by allowing the modelling of evaporation with fewer variables while maintaining accuracy similar to direct measurements. My study indicates under severe climate change that Lower Lake Winnipeg’s monthly mean evaporation may increase between 12-80%, which has significant implications for hydropower generation.