Riparian zones undoubtedly provide valuable ecosystem functions. Their proximity to water makes them vitally important areas. Thus, it is considered very important to identify and map the riparian areas. This study focuses on the importance of these areas by making attempts to delineate them using various methods and Geographic Information Systems. Three methods proposed in the international literature have been used for the identification and delineation of riparian areas. These methods have been developed and applied in the U.S.A. The first method (Holmes et al., 2011), is essentially a functional delineation model that relates the terrain relief with riparian areas. The second method (Wenger, 1999), focuses on slope, land use, potential flood – prone areas and wetlands. The third method (Abood et al., 2019,) is a more complicated method using a large number of criteria and a specific G.I.S. tool to determine the riparian zones. The methods have been applied to Pinios River basin with an area of about 10,000 km², located in Central Greece. The data used in the applied methods, were adapted to the corresponding data of the Pinios River basin. In order to assess and evaluate the results of the applied methods, it was necessary to find a benchmark method. This was the method proposed by the Copernicus program. According to this method, the riparian zones are delineated using high-resolution satellite data for the whole European Union. Comparisons were made for streams of different stream orders of Pinios River basin for the three applied methods. Each one was also compared separately with the riparian areas of the Copernicus method. The findings of the present work indicate that, methods based on fixed widths of riparian areas are inadequate, because they take into account only the river network and no other significant parameters such as geomorphology, vegetation and soil characteristics.

Water is one of the most important elements for every human being in our ecosystem. The earth's water level is falling, despite the growing need for water every day. Therefore, Shri. Amrishbhai Patel of Shirpur (Dist. Dhule, Maharashtra) invented a novel concept of Pani Adava pani Jirava permanently fix this problem. The majority of the Khandesh region realised in the summer that there was a water shortage. The inconsistent rainfall is also a major contributing factor. Deeper and deeper subsurface storage is being depleted because of farmers harvesting underground water for agriculture. Main goal is to save the rainwater we obtain during the monsoon and its retreat by utilising the Shirpur Pattern.

In Dhule district and Shirpur tahsil cover under black soil layers, acting as a barrier to water percolation and reducing the pace at which subsurface water sources recharged. It permanently resolves the issue of water scarcity. Here, the key idea is to divert extra water into canals rather than allowing it to run into the Tapi River. Cement Nalla Bandhara (CNB) is the building that accomplishes these goals. Today, 305 CNBs have been constructed and these dams prevent the extra rainwater from being used carelessly and unevenly. It allows the barest amount of water for farming. The water that was being drained from the Tapi River is now stored by CNB.

In the past, farmers could only harvest one crop annually from their fields. Fisheries is going to be a new sector shortly. Fish are cultivated using the effluent as fertiliser. Such happiness will come from farming, and the next generation of farmers will double their earnings. Making Shirpur one of India's towns with the quickest rate of growth. In addition to fortifying the foundation, the innovative idea will support future prosperity with honour.

Introduction: Access to clean and safe drinking water is essential for human health. However, in many regions, including Southwest Nigeria, drinking water contamination remains a critical issue. Lead (Pb) contamination, in particular, poses significant health risks, such as neurological damage and developmental issues in children. This research is novel as it provides a comprehensive assessment of lead levels in drinking water across a diverse region with varied socio-economic activities and environmental conditions. The primary aim of this study is to assess lead (Pb) contamination levels in drinking water samples from Ondo, Osun, and Ekiti States in Southwest Nigeria and the contamination factor (CF), enrichment factor (EF), and index of potential ecological risk (Eif). Methods: Water samples were collected from various sources, including wells, boreholes, rivers and streams, in the locations. The samples were analyzed for lead content using Atomic Absorption Spectroscopy (AAS). Results: The lead levels (0.003-0.077 mg/L) were then compared with National Environmental Standards and Regulations Enforcement Agency (NESREA) and World Health Organization (WHO) safety standards. The results indicated that a significant proportion of the water samples contained lead levels exceeding both NESREA and WHO safety standards. The results also depicted: All samples but one exhibited low Degree of Contamination and Contamination Factor, low EF and Eif. The highest contamination level was found in sample from a stream, likely due to proximity to a car wash shop, nearness to a moderately busy road, and the use of lead-containing materials in water distribution systems within the environ. Conclusion: The study concludes that lead contamination in drinking water is a pervasive issue in Southwest Nigeria, posing serious public health risks. The findings underscore the need for regular water quality monitoring, public awareness campaigns, and the implementation of stricter regulations to ensure safe drinking water.

Vegetation plays a vital role in natural open channels such as rivers, streams or artificial channels. In this work, a turbulent, sub-critical, open-channel flow (Fr = 0.2) with submerged rigid vegetation is numerically studied, using the ANSYS FLUENT code. The VOF method for free-surface treatment and the standard k-ε turbulence model were used for the numerical simulation of the flow. The vegetation was modeled as vertical, rigid cylinders fixed at the bottom of the channel. Regarding the arrangement of the stems, two cases were examined. In the first case, a single vegetative stem was considered o be in the center of the channel, while in the second, a series of three equidistant vegetative stems were located transversely at the center of the channel. In both cases, the height of the stems was equal to one third of the flow, based on the normal depth of the corresponding open-channel flow. The results showed that the vertical velocity profile in the submerged vegetation layer is significantly different from that in the upper non-vegetation layer, which reasonably followed the log law. Specifically, the vertical velocity profile for the submerged vegetation layer shows an ‘S’- shaped curve with its inflection point being close to the vegetation height. Furthermore, a slight drop in the free-surface where the vegetative stems are developed was observed. The structure of turbulence essentially affected in the neighborhood of the stems, leading to higher values of eddy viscosity at the near-crest area of each stem.

The composition of bacterial communities typically varies greatly in freshwater ecosystems, and the presence of toxic cyanobacterial blooms poses significant concerns for ecosystem and human health. In this study, we aimed to monitor the vertical bacterial community profile in a natural lake used for drinking water. We collected samples in March 2023 from the water column (surface, 1 m, 5 m, 10 m, 15 m, 20 m) where water is abstracted for the drinking water treatment plant. The bacterial community profile was examined by 16S rRNA amplicon sequencing using MinION Mk1C (ONT). The water temperature was 9.8°C ± 0.37, the dissolved oxygen level was 11 mg/L ± 1.44, and pH was 7.2 ± 0.27 as expected for the spring turnover period. The bacterial community was dominated by Cyanobacteria and its abundance decreased gradually throughout the water column in which the maximum abundance was 88% at the surface, while the minimum was 73% at 20 m. The result was in line with the historical data of this lake, showing that Cyanobacteria were an important component of the microbial community. The majority of the Cyanobacteria reads were assigned to Planktothrix rubescens. Toxic Planktothrix rubescens blooms have been observed in Lake Sapanca since the 1990s, highlighting the critical need for ongoing monitoring studies. Planktothrix agardhii and Synechococcus sp. were also detected at all depths. Moreover, the abundance of Pseudomonadota increased throughout the water column, reaching a maximum of 18% at 20 m. The results indicated that Cyanobacteria, which can produce toxins, taste, and odor, were abundant throughout the water column during the spring turnover together with Pseudomonadota and Actinomycetota species, which can degrade complex organic compounds. Since the study area is a drinking water resource, monitoring studies integrated with a metagenomics approach are needed to effectively mitigate Cyanobacteria blooms and microbial community dynamics.

INTRODUCTION

In ancient times, silver played an important role in curing many diseases and fighting against many infections. Currently, silver is used in AgNPs for targeting many biomedical and physio-chemical reactions to fulfill research goals. But many drawbacks are also reported in AgNP reactions, like allergy and environmental risks, so to avoid all these side-effects, plant-based AgNPs are synthesized by researchers. In our research, we have used silver nano-particles of Phyllanthus emblica and Adhatoda vasica Leaf Extract and conducted a comparative study on microbes.

METHODS

Leaf samples were first collected, then crushed into a powder. Next, we made a water-based extract solution by heating the mixture to 80 degrees Celsius for three to four hours using a magnetic stirrer. Finally, the leaf extract was combined with 1M silver nitrate solution, which was made by dissolving 1.7 grams of silver nitrate in 100 milliliters of water. Finally, the mixture of amla and adusa silver nitrate was centrifuged at 12000 rpm for 30 minutes, discarding the supernatant and collecting the dark pellet to form AgNPs of leaf extract. Finally, the leaf extract was collected in the form of a powder and dried for two to three days in a dark place. Using the disc diffusion and well diffusion methods, we investigated the effects of these AgNP powders at varying concentrations against disease-causing bacteria, such as E. Coli, S. Aureus, Mucor, and Aspergillus strains. Additionally, we utilized the commercial antibiotic streptomycin to complete the comparative study.

CONCLUSIONS

The presence of secondary metabolites in plant leaves makes plant-based drug systems and AgNP molecules more effective and eco-friendly as compared to chemical-based AgNPs. In our research, AgNPs of amla and adusa leaf extracts were investigated in a comparative study on microbes with positive results compared to the commercial antibiotic streptomycin in terms of killing microbes, as was clearly shown by the formation of a zone of inhibition on Petri plates .

The chemical pollutants and particulate matter resulting from the copper smelting process have severe impacts on air quality and human health. In this context, modeling air pollution from copper smelting plants has been recognized as a powerful tool for analyzing and predicting the dispersion of pollutants and assessing their effects on the environment and public health. This study aims to present optimization and control methods for mitigating air pollution from copper smelting plants and to examine the impacts of the Khatunabad smelting plant on the climatic parameters of the Khatunabad plain. The climatic parameters such as temperature, precipitation, soil moisture, and wind, derived from the ERA5-Land database, along with vegetation data from MODIS sensors, were analyzed in the Khatunabad plain. Subsequently, the dispersion of pollutants emitted from the Khatunabad smelting plant's chimney was modeled using climatic data and the AERMOD software. The results indicated that the concentration and behavior of pollutants were strongly correlated with wind components. Therefore, modeling the behavior of pollutants while accounting for wind patterns at different hours, months, and seasons can serve as an effective tool for analyzing and predicting the dispersion of pollutants from the copper smelting plant in the Khatunabad plain. The maximum concentration of total suspended particulate matter in this modeling was estimated at 0.48 μg/m³, while the maximum concentration of SO2 was estimated at 0.95 μg/m³. Additionally, based on the modeling results, the highest accumulation of pollution was observed in the northeastern and southwestern sections of the plant.

The prediction of the dynamics of mollusk farming in Santa Catarina, Southern Brazil, in response to environmental changes is essential for ecosystem management and the implementation of public policies. Climate changes or the carrying capacity of the environment can affect commercial exploitation and quantify the environmental impacts of farming. Computational mathematical models, such as MOHID Bivalves, have been used to analyze these issues, combining descriptions of ecological and physical processes to solve complex problems. MOHID, based on a computational grid to solve mass transport equations, was coupled with a biogeochemical module based on DEB theory, which describes the physiological response of an organism to environmental changes. The objective of this research was to evaluate the hydrodynamic numerical tool MOHID Bivalves applied to aquaculture, focusing on predicting the growth of Crassostrea gigas in the bays of Santa Catarina Island, Southern Brazil. Parameterizations and numerous simulations were performed to adjust the model to field data between 2013, 2015, and 2019 in the southern and northern locations of the bays. Preliminary results showed that the model satisfactorily reproduced the growth dynamics in shell length of C. gigas at both study sites, even without site-specific calibration. Mathematical models with hydrodynamic numerical simulation are the best option to accurately predict the dynamics of mollusk farming in response to natural or anthropogenic changes, whether for optimizing commercial exploitation or quantifying environmental impacts. It was concluded that the model is suitable for the cultivation configurations of C. gigas in the bays of Santa Catarina Island, Southern Brazil, providing a powerful tool to sustainably optimize aquaculture practices. The next stages of the study include applying this parameterization associated with the water quality model to predict the carrying capacity of the bays and predictive studies of better farming scenarios.

Machine learning models have shown promising results for streamflow prediction. However, they are commonly difficult to interpret because they are not based on physical principles but on the relationship between inputs and outputs. Consequently, this work proposes to evaluate two main aspects: First, the advantages and drawbacks of the methods for assessing the influence of the variables inside a machine learning model for short-term streamflow prediction. Second, whether the influence of variables has an acceptable physical interpretation. In this sense, a Random Forest model was trained with the Upper Ter Catchment (Spain) data. The model employs different accumulated precipitation and streamflow variables. The methodology evaluates the influence of the variables globally, by intervals, and in specific predictions. The results show that the mean decrease in accuracy, the mean decrease in node impurity and the mean SHAP (Shapley Additive Explanations) confer the main influence to a similar group of variables. Nonetheless, the Tornado method shows discrepancies. The partial dependence analysis results suggest that this method cannot accurately portray the influence of the variables in poorly represented ranges. In contrast, the ALE (Accumulated Local Effects) results acceptably capture the influence of the variables for the whole data spectrum. The Shapley and SHAP values indicate that the accumulated precipitation in less than six hours acquires the highest importance during the rising limb of the hydrograph, which is consistent with the catchment system. During the falling limb, the streamflow values closer to the output horizon are the most influential, suggesting that the shape of the limb is guided by them. Machine learning models for streamflow prediction can obtain physically acceptable relations between variables and outputs, which may lead to a further description of the catchment system. However, the methods used to represent the influence of the variables must be selected carefully to avoid misleading interpretations.

As urban areas confront the escalating challenges posed by climate change, population growth, and environmental degradation, developing resilient and sustainable water management systems has become crucial. This study examines a comprehensive approach to enhancing urban water resilience through the integration of smart technologies, holistic management frameworks, and green engineering solutions. Smart technologies, including advanced sensor networks and real-time data analytics, are revolutionizing urban water management by facilitating dynamic monitoring, predictive maintenance, and optimized resource allocation. These innovations improve flood forecasting, reduce water losses, and enhance distribution efficiency. Complementing these technologies, a holistic management approach that merges storm water management, wastewater recycling, and potable water systems within a unified framework is demonstrating significant improvements in resource efficiency and environmental sustainability. This integrated strategy supports the development of adaptable water infrastructure capable of addressing diverse urban stresses. Additionally, green engineering solutions, such as green roofs, permeable pavements, and constructed wetlands, offer effective storm water management while also reducing urban heat islands and enhancing biodiversity. By synthesizing recent research and practical applications across these domains, this paper provides a forward-looking perspective on building robust, adaptive urban water systems. The integration of smart technologies, comprehensive management practices, and green engineering solutions contributes to advancing urban water resilience and sustainability in the face of ongoing and future challenges.