Sustainable water supply is crucial to meet the increasing demand for drinking water and water resources worldwide. A serious challenge in this context is the presence of high nitrate levels in water sources due to agricultural activities and human impacts.

Nitrate is a common anion found in water as a result of agricultural fertilization and other human activities. While nitrate itself is not harmful to health, it can cause health problems at high concentrations in drinking water sources or industrial water reservoirs. The limit value in groundwater is 50 mg/L according to the European Union’s Groundwater Directive 2006/118/EC, and countermeasures are to be initiated at a value of 37.5 mg/L. Most methods to reduce the nitrate level, however, are relatively expensive.

One promising method for reducing nitrate levels in water could be the cultivation of plants in deep-water culture systems. In this approach, plant roots are placed in containers filled with water, whereby the plants are able to absorb nitrate from the water and use it for their own growth. In this way, an environmentally friendly alternative for the food supply by cultivating vegetable plants could be achieved in addition to the actual goal of processing groundwater and drinking water. The present experiment aims to test the effectiveness of deep-water culture as a method for reducing high nitrate levels in water. Several series of tests were carried out with different nitrate concentrations and are presented in this poster, giving an overview of possibilities for nitrite reduction and the limits of the measurements.

Introduction: Streams in Nigeria, as well as globally, are indispensable resources that are vital for supporting livelihoods and cultural heritage. The disappearance of streams in Nigeria has multifaceted consequences and necessitates urgent attention for future endeavors. Climate change-induced alterations in precipitation patterns, prolonged droughts, and deforestation are contributing factors to the depletion of streams across various regions in Nigeria. This phenomenon has detrimental effects on biodiversity, water availability, agricultural productivity, and community resilience. The loss of a stream threatens aquatic species, exacerbates water scarcity, disrupts essential ecosystem processes, diminishes agricultural yields, and increases vulnerability to climate-related risks. Methodology: This study involved a meticulous examination of the current literature and systematic review procedures. Steps such as assessing the current state of streams in Nigeria, acknowledging the review's importance within Nigeria's context, identifying consequences such as the disappearance and degradation faced by the streams, and exploring future research directions and potential interventions for sustainable stream management were taken. Results: To tackle these challenges, it is crucial to implement sustainable land management practices, promote water conservation, develop climate change adaptation strategies, adopt integrated water resource management strategies, reform policies, and invest in research. By prioritizing these actions, Nigeria can mitigate stream disappearance's adverse impacts, enhance ecosystem resilience, safeguard water resources, and promote sustainable development despite climate change uncertainties. Conclusion: The disappearance of streams in Nigeria amidst climate changes has significant consequences for both the environment and society. The effects range from biodiversity loss to increased vulnerability to climate-related risks, exacerbating water scarcity, and impacting agricultural productivity.

River basin management, the backbone of many economies, relies on understanding historical and projected fluctuations of water budget components: precipitation, evapotranspiration, runoff, soil moisture, which result decline of water availability in the world particularly Northern Ethiopia. Currently, the use of satellite remote sensing information is becoming more critical for acquiring hydrological information in ungauged areas, complementing missing values, and measuring hydrological components/ water budget components in large-scale areas. A concurrent mixed research methodology has been used in this study. The hydrological components are processed using formulas and software, which yields a map and numerical data that require a thorough explanation. The non-gridded statistical data for each component of the water budget was extracted using spatial analytic tools that were accessible in the GIS /Geographic Information System. Rainfall was localized downstream of the study during all research periods, Except for a small amount in 2023, when evapotranspiration varied and was focused in the southeast of the region, soil moisture levels throughout all periods were concentrated in the west. Evapotranspiration was in line with rainfall. This might be the outcome of the study's upper stream, the western section, having a lower temperature than the downstream. As evidence of this, the evapotranspiration map indicates that it was low in the upper stream (Western region) and high downstream (Eastern region). The findings of this study also show that runoff increases as precipitation increases. Rainfall has increased in intensity, which has saturated the soil and expedited runoff. The paper's unique findings, which have values of 5.355 MMC/million-meter cube, 6.960 MMC, 0.882 MMC, and -4.700 MMC, respectively, demonstrate that the trend of the water budget has been declining during the years 1993, 2003, 2013, and 2023. Accordingly, there was a significant potential for evapotranspiration, resulting in a water shortage.

This study embarks on a comprehensive analysis of a regional Water Supply System (WSS), focusing on the development of predictive models to support the management and modernization of the system. Highlighting the need for advanced predictive models in the face of aging infrastructure and variable demand patterns, I assess the effectiveness of modernization efforts within a WSS through operational data analysis. Methods: Utilizing a case study approach, we applied statistical analyses to failure and recovery data, aiming to extract characteristic parameters crucial for predictive modeling. The data included failure frequencies, recovery times, and their impact on WSS performance. Results: The analysis yielded a global model predicting WSS availability and reliability, incorporating failure and recovery rates to evaluate the system's operational efficiency. The findings demonstrate the model's potential to predict WSS performance, guiding decision-making for future infrastructure investments and operational strategies. Conclusions: The predictive modeling approach offers significant insights into the WSS's management, indicating that such methodologies can enhance the reliability and efficiency of water supply systems. Future work should focus on refining these models with more extensive datasets, considering the variability of external factors like climate change and urban growth on system performance. The predictive modeling approach offers significant insights into the WSS's management, indicating that such methodologies can enhance the reliability and efficiency of water supply systems. Future work should focus on refining these models with more extensive datasets, considering the variability of external factors like climate change and urban growth on system performance. This underscores the importance of integrating predictive models into the strategic planning and operational frameworks of water supply systems to ensure sustainable water management in response to evolving environmental and societal demands.

In Pakistan, one of the basic challenges the urban areas are facing is the lack of high-quality water for people due to rapid population growth, unplanned urbanization and increasing climate uncertainty. The Indus river system is the major source of water for people and agriculture in Pakistan. Due to dramatic population growth, water demand has also increased but water availability is declining so sharply that in future urban water management may meet with severe challenges. Pakistan is the eighth most climate-vulnerable country in the world. This extreme climatic variability has intensified the need to manage Pakistan’s water resources more sustainably. In 2018, the government of Pakistan developed a National Water Policy (NWP) to provide the guidelines for the development of sustainable water management solutions. To implement this policy, there is a decision support tool called the Urban Water Optioneering Tool (UWOT), which aims at facilitating the selection of combinations for water saving technologies and supporting the implementation of integrated and sustainable water management in new developments. The tool is based on a water balance model which enables the investigation and analysis of interactions within the major urban water cycle streams. The technology options are determined by a genetic algorithm (GA) allowing the efficient exploration of decision possibilities. GA is applied to address multiple contaminants in the water system. In essence, UWOT is a tool that aids decision-making by finding the best combination of water-saving technologies to ensure sustainable water management in urban developments. It has been successfully tested in the UK. This tool considers both the quantitative data and qualitative insights, and provides a complete picture of challenges in water management.

We employ regional analytic techniques to estimate suspended sediment load in watersheds that lack statistics due to their size and the absence of sediment measuring stations. In order to model the suspended sediment load for the Poonel watershed in Gilan and the Kowter watershed in West Azerbaijan province, this research used gene expression planning (GEP) methods, an adaptive network-based fuzzy inference system (ANFIS), support vector regression (SVR), and autoregressive integrated moving average (ARIMA). Finally, a comparison was made between these methods. For this purpose, information was gathered from 1979 to 2016 from the hydrological stations at Poonel and Kowter, as well as data on the flow rate, sediment discharge, precipitation height, minimum, maximum, and average temperatures of two synoptic stations at Bandar Anzali and Mahabad. Any precipitation that occurred on a day with a mean temperature below zero was classified as snowy precipitation in order to identify the kind of regime. We took into consideration the rainfall regime of the Poonel watershed (0.02% snowfall) and the snow regime of the Mahabad watershed (10% snowfall). The objective was to simulate the flow rate of sediment through five inputs: precipitation height, minimum temperature, maximum temperature, average temperature, and flow rate. The findings indicated that the SVR with polynomial kernel was the best model for the Kowter basin (snow regime), with a root mean square error of 0.49 and a coefficient of explanation of 0.74; similarly, the Poonel basin (rain regime) exhibited an optimal model for the SVR with polynomial kernel, with a root mean square error of 0.42 and a coefficient of explanation of 0.75. After all 14 models were ranked, it was discovered that the models associated with the Poonel basin performed better; these models had ranks ranging from 1 to 5. This indicates that model performance is affected by a weighted precipitation regime.

Data on land cover areessential for many facets of life, including politics, economics, and science. Since timely and precise land cover information is essential to the correctness of all subsequent applications, it is highly sought after. The purpose of this work is to select the better LULC classifier and investigate change detection using a more accurate classifier. Support vector machine (SVM) and random forest (RF) algorithms were applied to categorize LULC satellite data in the watershed. Six LULC classifications comprised the research area: bare lands, forests, shrublands, waterbodies, settlements and agricultural lands. SVM and RF have overall classification accuracies of 87.46% and 91.19%, respectively, and RF was selected for change detection analysis. According to the results, there was a growth in agricultural land of 6.44% between 2002 and 2012 and 14.94% between 2012 and 2022. Between 2002 and 2012, the settlement area grew by 72.17%, and between 2012 and 2022, it expanded by 21.44%. The forest saw a 48.27% decrease from 2002 to 2012 and a 14.94% gain from 2012 to 2022. Shrub land decreased by 8.16% between 2002 and 2012 and by 26.30% between 2012 and 2022. Additionally, there was a change in bare land between 2002 and 2012, when it increased by 74.05%, and between 2012 and 2022, when it decreased by 41.42. Consequently, utilizing an RF algorithm to detect changes in LULC is a crucial method for classifying multispectral satellite data to comprehend the best way to exploit natural resources, implement conservation measures, and make decisions regarding sustainable development. The study results provide useful information for policymakers and planners in the implementation of sustainable land resource planning and management in the context of environmental change.