Water Quality (Engineering)

Global environmental change and increased resource consumptionare driving the need for urban water transformations. The complexity of this challenge calls for strategic action to advance alternative governance and citizenship practices. This paper reviews literature on social innovations and social entrepreneurship and conceptually integrates it with insights from sustainability transitions scholarship to explain how social innovators and entrepreneurs influence transition dynamics. It proposes seven core ingredients (social mission, creative innovations, entrepreneurialism, social networks, hybridisation, social value-creation, and social change) and four key processes (opportunity recognition, resource mobilisation, empowering and embedding, and learning and adaptation) to explain the influence of the social innovation and entrepreneurship within the context of inadequate sanitation in developing countries. The results show that social innovations and entrepreneurships affect transformational changes in governance and citizenship while creating multi-dimensional value in the areas of social inclusion, economic development and environmental sustainability.

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

The Hawkesbury–Nepean River System (HNRS) is one of the most important inland river systems in Australia, which supplies over 90% of Sydney's potable water. In this paper, 25 water quality parameters from nine sampling stations in the HNRS covering a period of 12 years are used to examine the trends in the water quality data in the HNRS. It has been found that there is an overall increasing trend of turbidity, chlorophyll-a, alkalinity, total iron, total aluminium, total manganese and reactive silicate, indicating an overall water quality deterioration in the HNRS during the last decade. The parameters such as phosphorus, suspended solids and ammonical nitrogen do not show any marked change over the period of study. Although an improvement in water quality can be seen at some stations downstream of the undisturbed parts of the catchment, there is a clear trend of increased chemical and physical water quality deterioration at many locations in the HNRS. Better land use planning i...

Dissolved Oxygen (DO) reaeration rate amount is an important indicator for measuring water quality and ecosystem health in water bodies. In this study, a model was developed to simulate the DO reaeration rate coefficient (Ka) temporal distribution for the Hilla River headwater at the Saddat Al-Hindiyah Reservoir, Iraq. The DO transfer rate at the reservoir water surface was determined uniquely by implementing the explicit forward time numerical scheme to simulate the Ka variation along with DO based on monthly field measurements of DO and temperature during the study years (2021 and 2022). With low statistic errors, the results showed that there is good agreement between the DO numerical determination, field data and the analytical solution, giving headwater reaeration coefficient value of 0.1 day-1 at 20℃ and temporal values of (0.091-0.128) day-1 and (0.086-0.128) day-1 during 2021 and 2022, respectively. The Ka temperature dependency, which is a direct relationship, led to high DO levels during winter and low during summer. Thus, it is necessary to vary Ka based on temperature for DO modeling applications, especially for water quality management purposes in rivers, lakes and reservoirs where Ka is variable during the year.

Application of neural-network methods revealed relationships between hydrological and hydrochemical characteristics of water flow, suggesting structural self-organization of substances dissolved in water in the form of micro layering. In particular, the coefficient of correlation between the concentrations of such substances in some cases reaches its nearly maximal value (0.99), combining with the high weights of neural network edges. This can be supposed to be due to the mechanical and chemical interactions in river flow with the participation of Van der Waals forces, hydration, and sorption. Other factors, not taken into account, can also have their effect, in particular those responsible for the fluctuations of the parameters of order, determining the singular contributions to the dynamic characteristics of the non-linear system under consideration. Such can be the cyclic oscillations of the characteristics under control with an amplitude decreasing with a decrease in the intensity of the pollution/selfpurification processes in water medium and increasing with an increase in this intensity. The obtained information, in addition to its direct purpose as a means to study the nature and properties of fresh water, is a necessary condition for the effective control of water resource quality and water management activity.

This study investigated the distribution of heavy metals in vegetative biofiltration columns irrigated by synthetic greywater. Twelve species of ornamental plants (three plants from each species) were planted in the same designed 36 biofiltration columns. Samples of effluent water, soils, roots, shoots and leaves were collected and analyzed. It was observed that before irrigation, the distribution of copper was in soils (0%), roots (42%), leaves (37%) and shoots (21%). After irrigation, this distribution changed to soils (29%), roots (39%), leaves (17%) and shoots (15%). It was found that lead concentrations decreased in soils from (84% to 7%), but increased in plants (from 16% to 93%) following irrigation with greywater. In contrast, the distribution of zinc changed from leaves (46%), roots (22%) and soils (16%) before irrigation to 89% in leaves and soils and 11% in shoots following irrigation. The chromium distribution before and after irrigation was found to be almost unchanged ...

Poor water quality is a serious problem in the world which threatens human health, ecosystems, plant and animal life. Prediction of surface water quality is a main concern in water resource and environmental systems.The lake Nokoué in Benin, the country's main water body, is experiencing without equivox pollution. Usually, the water quality index is evaluated manually by complex mathematical formulas with major risks of errors. This study discusses the development and validation of a Random Forest and Artificial Neural Network (ANN) model in estimating water quality index (WQI) in the lake Nokoué.The two models have been developed and tested using data from 20 monitoring stations over a period of 12 months. The modeling data was divided into two sets. For the first set, RF and ANN were trained, tested and validated using 12 physical-parameters as input parameters. A detailed comparison of the overall performance showed that prediction of the random forest (RF) model was better than artificial neural networks with coefficient of correlation (R 2)=0.98, root mean squared error (RMSE)=0.12, explained variance score (EVS)=0.98 and mean absolute error (MAE)= 0.14 at training phase while and at the validation phase their values are0.80, 0.19, 0.23, 0.74 respectively which demonstrates that RF is capable of estimating WQI with acceptable accuracy. This method simplifies the calculation of the WQI and reduce substantial efforts and time by optimizing the computations. This will help in taking appropriate preventive measures to control the water quality of lake Nokoué through associated chemical treatments.

In the former Carbochimica site of Fidenza, a bioremediation approach was developed using the technique of biodegradation of pollutants thanks to a selected autochthonous bacterial-fungal consortium. The soil was heavily polluted up to values of total hydrocarbon equal to 1800 mg/kg. The consortium was selected from the microorganism living in the Fidenza soil, bioaugmented and finally reinoculated in the bio-pile for soil treatment. The approach is absolutely innovative, due to the presence not only of bacterial strains but also for the use of fungal strains operating in synergy with the bacteria. The first data from the trials show an effective soil remediation performance. The LCA analysis allowed to make a global assessment of the environmental impacts of the bio-pile remediation treatment scenario compared to the no-action scenario. Impacts were assessed on 18 impact categories at the midpoint level according to the ReCiPe method. For the bio-augmented bio-pile remediation, the results showed a value in the climate change category of 10 kg CO2 for each ton of remediated soil, and at the same time improvement in the categories relating to the toxicity at the local level.

The present study assessed water quality parameters and attempts to compare four different Water Quality Indexes (WQIs) for consistency, similarity and reliability in assessing the water quality of river Benue -an inland river- under wet and dry seasons. The results demonstrate that River Benue is continually being polluted in both dry and wet seasons by different sources, particularly domestic sewage and storm runoffs from farmlands. The quality of the water generally exceeded physiochemical and microbiological infection risk limits recommended in water quality guidelines concerning their use for domestic, recreational and irrigational purposes. Proper sewage treatment and river quality monitoring are needed to guard against hazards to public health and vulnerable river water resources. The WQIs applied were: CCME WQI, BC WQI, Dinius’ WQI and Weighted Arithmetic WQI. To evaluate the differences between these indexes, data on ten water quality parameters (Temperature, pH, total diss...

Lightning Talk for NSF SI2 PI Meeting 2018: Widespread disruption of drinking water distribution systems in Puerto Rico following Hurricane Maria poses a significant risk to human health. Thus, it is necessary to strategically archive and disseminate water resources data relevant to public health and environmental concerns. This project seeks to design and test a prototype scientific cyberinfrastructure that integrates existing hardware and software platforms for the storage and curation of water resources data and analytical tools following natural disasters that cause loss of public utilities.

Poster for NSF SI2 PI Meeting 2018: Widespread disruption of drinking water distribution systems in Puerto Rico following Hurricane Maria poses a significant risk to human health. Thus, it is necessary to strategically archive and disseminate water resources data relevant to public health and environmental concerns. This project seeks to design and test a prototype scientific cyberinfrastructure that integrates existing hardware and software platforms for the storage and curation of water resources data and analytical tools following natural disasters that cause loss of public utilities.

Coastal water quality assessment is an essential task to keep "good water quality" status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R 2 , and PREI metrics were used in this study. The tree-based DT (RMSE = 0.

In this study, six permeable pavement systems (PPS) with different subbase configurations were tested to evaluate their nitrogen attenuation capabilities from urban stormwater runoff. The PPS subbases were configured based on the following rationale for achieving enhanced physical, chemical and biological treatment of nitrogen compounds commonly found in urban stormwater: (i) a layer of natural zeolite to enhance nitrogen removal by ion exchange and adsorption; (ii) a layer of bark chips as a carbon donor to enhance biodegradation; (iii) a saturated zone to maintain required moisture content and reduced oxygen level for enhanced denitrification and (iv) thin sand layer(s) to limit oxygen transport for creating an anoxic zone within the PPS. A laboratory water sampling and testing program was undertaken from these PPS over a six-month period using synthetic stormwater with different pollutant concentrations while simulating varying rainfall intensities to represent typical design rainfall events considered in urban stormwater management. Our results demonstrate an improved denitrification in the PPS when a saturated zone is maintained and an organic carbon source is added in the PPS subbase. Compared with the traditional PPS, the new PPS is found to attenuate twice the amount of the total inorganic nitrogen in stormwater. Also, the attenuation of nitrogen compounds by the standard PPS is found to be reduced with rainfall duration (e.g., 54% attenuation during the first hour and only 3.5% during the 4th hour of the rainfall). The new PPS is found to attenuate 68% of total inorganic nitrogen even in the 4th hour of the rainfall event. The new PPS has the potential to become an effective stormwater quality improvement device that can contribute towards cleaner urban waterways.

This study assessed water quality (WQ) in Tongi Canal, an ecologically critical and economically important urban canal in Bangladesh. The researchers employed the Root Mean Square Water Quality Index (RMS-WQI) model, utilizing seven WQ indicators, including temperature, dissolve oxygen, electrical conductivity, lead, cadmium, and iron to calculate the water quality index (WQI) score. The results showed that most of the water sampling locations showed poor WQ, with many indicators violating Bangladesh's environmental conservation regulations. This study employed eight machine learning algorithms, where the Gaussian process regression (GPR) model demonstrated superior performance (training RMSE = 1.77, testing RMSE = 0.0006) in predicting WQI scores. To validate the GPR model's performance, several performance measures, including the coefficient of determination (R 2), the Nash-Sutcliffe efficiency (NSE), the model efficiency factor (MEF), Z statistics, and Taylor diagram analysis, were employed. The GPR model exhibited higher sensitivity (R 2 = 1.0) and efficiency (NSE = 1.0, MEF = 0.0) in predicting WQ. The analysis of model uncertainty (standard uncertainty = 7.08 ± 0.9025; expanded uncertainty = 7.08 ± 1.846) indicates that the RMS-WQI model holds potential for assessing the WQ of inland waterbodies. These findings indicate that the RMS-WQI model could be an effective approach for assessing inland waters across Bangladesh. The study's results showed that most of the WQ indicators did not meet the recommended guidelines, indicating that the water in the Tongi Canal is unsafe and unsuitable for various purposes. The study's implications extend beyond the Tongi Canal and could contribute to WQ management initiatives across Bangladesh.

Un système d'alerte précoce des évènements de pollution des eaux de loisir est en développement, dans le cadre de trois projets de recherche. Le système est appliqué à l'estuaire du Tage et prend en compte la décharge du bassin versant d'Alcântara, le plus grand de Lisbonne. Ce système intègre explicitement la surveillance et la modélisation des différents processus physiques et de contamination, du bassin versant jusqu'à l'estuaire, aux échelles temporelles et spatiales appropriées. Ce travail inclut les activités suivantes: 1. la sélection de la zone pilote et la collecte de données; 2. la modélisation numérique (du réseau d'assainissement et de l'estuaire); 3. des levées expérimentales; 4. le suivi en ligne de paramètres hydrauliques et de qualité des eaux (réseau d'assainissement et estuaire); 5. l'intégration des modèles et des données de surveillance au sein d'une plate-forme informatique pour le contrôle en temps réel, basée sur une plate-forme existante pour l'hydrodynamique de l'estuaire (http://ariel.lnec.pt); 6. le développement d'un système-prototype d'alerte précoce basé sur le système intégré de modélisation et de surveillance en temps réel. Les trois premières activités sont sur le point d'être terminées. Des stations de surveillance de qualité des eaux sont en cours d'installation et l'intégration des modèles au sein de la plate-forme est en cours de développement. Cet article décrit la méthodologie et les résultats préliminaires de ce système d'alerte précoce.

This study investigated the distribution of heavy metals in vegetative biofiltration columns irrigated by synthetic greywater. Twelve species of ornamental plants (three plants from each species) were planted in the same designed 36 biofiltration columns. Samples of effluent water, soils, roots, shoots and leaves were collected and analyzed. It was observed that before irrigation, the distribution of copper was in soils (0%), roots (42%), leaves (37%) and shoots (21%). After irrigation, this distribution changed to soils (29%), roots (39%), leaves (17%) and shoots (15%). It was found that lead concentrations decreased in soils from (84% to 7%), but increased in plants (from 16% to 93%) following irrigation with greywater. In contrast, the distribution of zinc changed from leaves (46%), roots (22%) and soils (16%) before irrigation to 89% in leaves and soils and 11% in shoots following irrigation. The chromium distribution before and after irrigation was found to be almost unchanged ...

The IEWQI model is a reliable method for determining the accuracy, reliability, and affordability of transitional and coastal water quality. • Model could be efficient for optimizing model eclipsing and ambiguity problems in order to reduce model uncertainty • The uncertainty in the IEWQI model is <2 %. • The results of model evaluation metrics NSE and MEF reveal that the model could be ranked more accurately for water quality at each monitoring site. • Based on the findings, the performance of IEWQI applications reveals that suggested indicators might be adequate and reliable to monitor water.

Coastal water quality assessment is an essential task to keep "good water quality" status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R 2 , and PREI metrics were used in this study. The tree-based DT (RMSE = 0.

In this paper, a low cost, real-time water quality monitoring system which can be applied in remote rivers, lakes, coastal areas and other water bodies is presented. The main hardware of the system consists of off-the-shelf electrochemical sensors, a microcontroller, a wireless communication system and the customized buoy. It detects water temperature, dissolved oxygen and pH in a pre-programmed time interval. The developed prototype disseminates the gathered information in graphical and tabular formats through a customized web-based portal and preregistered mobile phones to better serve relevant end-users. To check the system effectivity, the buoy's stability in harsh environmental conditions, system energy consumption, data transmission efficiency and webbased display of information were carefully evaluated. The experimental results prove that the system has great prospect and can be practically used for environmental monitoring by providing stakeholders with relevant and timely information for sound decision making.

Streams play a key role in the global biogeochemical cycles, processing material from adjacent terrestrial systems and transporting it downstream. However, the drivers of stream metabolism, especially those acting at broad spatial scales, are still not well understood. Moreover, stream metabolism can be affected by hydrological changes associated with seasonality, and thus, assessing the temporality of metabolic rates is a key question to understand stream function. This study aims to analyse the geographical and temporal patterns in stream metabolism and to identify the main drivers regulating the wholeecosystem metabolic rates at local and regional scales. Using a coordinated distributed experiment, we studied ten headwaters streams located across five European ecoregions during summer and fall 2014. We characterized the magnitude and variability of gross primary production (GPP) and ecosystem respiration (ER) with the open-channel method. Moreover, we examined several climatic, geographical, hydrological, morphological, and physicochemical variables that can potentially control stream metabolic rates. Daily rates of stream metabolism varied considerately across streams, with GPP and ER ranging from 0.06 to 4.33 g O 2 m-2 d-1 and from 0.72 to 14.20 g O 2 m-2 d-1 , respectively. All streams were highly heterotrophic (P/R < 1), except the southernmost one. We found that the drier climates tended to have the highest GPP, while humid regions presented the highest ER. Between the sampling periods no statistical differences were found. Partial-least squares models (PLS) explained ∼ 80% of the variance in GPP and ER rates across headwater streams and included both local and regional variables. Rates of GPP varied primarily in response to the local variables, such as streambed substrate and stream water temperature. In contrast, regional variables, such as the mean annual temperature or the land use of the catchment, had more relevance to explain ER. Overall, our results highlight that stream metabolism depends on both local and regional drivers and show the positive experience of a young network of researchers to assess scientific challenges across large-scale geographic areas.

Pond agriculture tide is a combined freshwater, salinity and the result of agricultural activity. Nowadays, parts of the irrigation area especially secondary channel at agricultural tidal pond (with shallow water) was related to the movement of water flow. Therefore, the main important stage to study is analysis of flow and velocity pattern. Flow pattern at agricultural tidal pond was mostly influenced by input of water on tidal movement and effect of wind. In addition, velocity of wind would generate shear forces on the water surface that would push the water mass and create the flow movement. The flow and velocity pattern could be examined by utilizing a two dimensional (2-D) hydrodynamic numerical model. The model was based on numerical solution of continuity and momentum equations which was solved with finite different method that was Mac Cormack method. The validity of model was tested with experimental model of 2-D stream flow simulations with rectangular shape. The experiment...

Popularity of chloramine has been dampened by nitrification, which is believed to highly accelerate chloramine decay. This can seriously compromise the primary goal of using chloramine as a secondary disinfectant. Our previous laboratory-scale studies showed that highly accelerated chemical decay of chloramine was caused by soluble microbial products (SMPs) released by microbes under severely nitrifying conditions. To understand whether a similar phenomenon exists in full-scale distribution systems, samples were collected from four full-scale systems supplied from different water sources and have been compared with results obtained from laboratory-scale systems. The results verified that the acceleration typical in severely nitrified water is common in full-scale chloraminated systems under severely nitrifying conditions.

Maintaining longer lasting disinfectant residual in a distribution system is highly important to prevent microbial re-growth and hence to deliver safe drinking water. However, various factors such as microbes present in bulk water, sediment, or attached to pipe wall and biofilms accelerates the chloramine decay. Among them, biofilms are a major factor in accelerating chloramine decay as they provide a habitat for the microbes. Thus, this study investigates the effect of biofilms in terms of chloramine decay in the distribution system. Biofilms were grown under various chloramine residuals and different ages of biofilms were investigated by subjecting them to batch tests. Experimental results repeatedly showed that chloramine decay due to biofilms is independent of its growth condition, particularly for different chloramine residuals.

This project report has been prepared as an output of the Australia-Indonesia Centre Small Project (Infrastructure Cluster),&lt;i&gt; A socio-technical investigation of Jakarta&#39;s opportunity for leapfrogging towards sustainable water management to identify common and distinct principles and practices&lt;/i&gt;.

Due to the unavailability of electricity, about 85% of groundwater irrigation in eastern India employs fuel-powered surface pumps, which can have system efficiencies as low as 5%. As fuel prices continue to rise, impoverished smallholder farmers cannot afford the operation costs of their systems and, as a result, irrigate less land. This research aimed to develop a more efficient off-grid system that eliminates suction head while continuing to utilize a fuel engine on the surface. Design requirements included increased discharge, increased efficiency, portability, maintainability, availability of replacement parts, and affordability. Flow rates and efficiencies of Indian pumps at varying pressure heads were tested to establish baseline performance and test the hypothesis that removing suction lift reduces operation costs. It was found that eliminating suction head can decrease operation costs up to 44% for the farmers. Fuel-driven system options investigated include flexible shafts and telescoping shafts to transmit power from a surface engine to a submersible pump, fluid machinery such as semi-open hydraulic systems, jet pumps, compressed air motors, and air lifts, and off-grid electricity generation employing a household backup generator or automotive alternator. However, none of these alternatives met all the design requirements. Instead, a hybrid motorized-manual rope pump was prototyped and tested on a well in Ruitola, Jharkhand. Although this system does not meet all the design requirements, it has the added benefit of providing domestic water supply. The prototype discharged an average of 155.4 L/min in motorized mode and 17.2 L/min for men and 13.3 L/min for women in manual mode. The rope pump received positive feedback from the users and thus could be taken forward with several modifications to improve performance. To increase the available water resources, evaporation reduction strategies to limit water loss from farm tanks were explored. The strategies employed waste materials and included covering a water surface with waste PET bottles and floats comprised of PET waste bottles and old saris. While waste bottles proved promising in a preliminary test, data in a larger experiment has thus far proved inconclusive and further testing is needed.

Due to the unavailability of electricity, about 85% of groundwater irrigation in eastern India employs fuel-powered surface pumps, which can have system efficiencies as low as 5%. As fuel prices continue to rise, impoverished smallholder farmers cannot afford the operation costs of their systems and, as a result, irrigate less land. This research aimed to develop a more efficient off-grid system that eliminates suction head while continuing to utilize a fuel engine on the surface. Design requirements included increased discharge, increased efficiency, portability, maintainability, availability of replacement parts, and affordability. Flow rates and efficiencies of Indian pumps at varying pressure heads were tested to establish baseline performance and test the hypothesis that removing suction lift reduces operation costs. It was found that eliminating suction head can decrease operation costs up to 44% for the farmers. Fuel-driven system options investigated include flexible shafts ...

Water quality involves complex relationships between numerous parameters due to environmental, demographical, and anthropogenic adaptabilities. The presence of computational techniques provides solutions to the limitations of empirical models and derives the precise relation between parameters. The rate of reaeration is an indicator of the health of the water stream that requires accurate measurement. This study employs an adaptive neuro-fuzzy inference system (ANFIS) machine learning technique to predict the reaeration coefficient under varying hydrodynamic and water quality conditions. The fiveyear dataset prepared on Yamuna River, Delhi, India, to reflect the characteristics of the highly urbanized river, receiving high organic load from multiple point sources. The ANFIS models are developed using a combination of different input parameters. The data is classified using grid partitioning and subtractive clustering methods and trained on the Gaussian membership function type. The performance evaluation of developed ANFIS models is performed through correlation coefficient and root mean square error (RMSE). The results suggested that a robust rule base and flexible approach of ANFIS produces a highly accurate model that could be used to develop strategic schfemes of waste load carrying capacity assessment and water resource management plans.

Coastal water quality assessment is an essential task to keep &amp;quot;good water quality&amp;quot; status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model&amp;#39;s reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models&amp;#39; performance, the RMSE, MSE, MAE, R 2 , and PREI metrics were used in this study. The tree-based DT (RMSE = 0.

La calidad del agua se clasifica en diferentes categorías en función a la presencia de fuentes contaminantes, en el 2018 el Perú adoptó el modelo canadiense “ICA-PE” instrumento con el que se puede evaluar la variación de la calidad de las aguas superficiales. En el presente estudio se evaluó la variación de la calidad del agua del río Coata en el tramo puente Maravillas –  Puente  Coata  debido  a  la  existencia  de  fuentes  contaminantes.  Para  establecer la variación de la calidad del agua se identificaron los puntos de muestreo: ACFC-01, DCFC-03 y DCFC-05 y las fuentes de contaminación: FC-02, y FC-04, a partir de los cuales se analizaron 18 parámetros requeridos para el cálculo del ICA-PE, los parámetros se analizaron de acuerdo a los métodos normalizados para el análisis de agua potable y residual (AWWA-APHA-2001). Las aguas del río Coata en el tramo puente Maravillas y puente Coata se encuentran contaminadas principalmente por materia orgánica, la fuente contaminante FC-02...

Lakes are traditionally under-valued resources to human society but good water qualities in lakes are essential for maintaining recreation and fisheries and for the provision of municipal drinking water. This study was conducted to investigate the present water conditions and pollution sources of two selected lakes in Dhaka city, which are Uttara Lake and Gulshan Lake. For exploring the problems and the reason of pollution of these lakes, the surrounding environment of the lakes had been thoroughly surveyed where physical and chemical water quality parameters at some selected points had been tested. A questionnaire survey of a total of 200 people was made to find out the major reasons for lake water pollution from 2 nd January 2022 to 15 th January 2022. Physical and Chemical Parameters of water such as pH, Conductivity, DO, Iron, Temperature, Color, Alkalinity, CO 2 , Salinity, Turbidity, BOD 5 were analyzed at different three locations of each lake. Finally, the mean value of such respective parameters was compared with the water quality standards as set by the Bangladesh Standards for Drinking Water (ECR 97), WHO Guideline Values (1993), and Bangladesh Standards for Fisheries (EQS, 1997) to find out the present water quality of these selected two lakes.

services are more generalizable than cultural services. Particularly in natural and semi-natural systems, spatial gradients need to be considered. We discuss uncertainties associated with this matrix-based EI and ES assessment approach and suggest that future large-scale studies should include additional land cover information and ecosystem disservices and may determine ES fluxes by differentiating between ES provision and consumption.

Consumption of untreated microbiologically contaminated water, prevalent in many developing countries, exposes the vulnerable population to inordinate health risks including most life-threatening diarrhoeal diseases like cholera and other illnesses. In response, innovative techniques for treating water at the household level, known as household water treatment (HWT), have emerged as practical and cost-effective solutions. Solar water disinfection (SODIS) has gained recognition as one such method that utilizes sunlight to inactivate harmful micro-organisms in water. This review examines the effectiveness of SODIS as an HWT technique by analysing scientifically robust evidence documenting its microbiological efficacy and the positive health gains among SODIS users. It explores the challenges and limitations that impact its inactivation efficiency and sustainability. Its novelty lies in its expanded exploration of the available strategies and techniques for enhancing the effectiveness ...

This work designed and fabricated a prototype digital water level controller using
ultrasonic sensor interfaced with microcontroller. The supply and overhead tanks were
designed and fabricated with fiber glass of thickness 5mm. The electric pump was
selected based on the capacity of the overhead tanks and its distance from the supply
source. Polyvinyl Chloride (PVC) pipes were selected based on its carbon content,
low cost, chemical resistance and ease of joining. The electronic circuit was designed
and built using ultrasonic sensor, microcontroller, transistors and relays. Result
obtained on testing shows that the controller regulates the ON and OFF of the pump
depending on the water levels in the overhead tank. At the highest discharge level of
the electric pumping machine which was 900m, the efficiency of the sensor response
was found to be 88.9%. This shows that a good result was on the prototype design.

The Hawkesbury–Nepean River System (HNRS) is one of the most important inland river systems in Australia, which supplies over 90% of Sydney&#39;s potable water. In this paper, 25 water quality parameters from nine sampling stations in the HNRS covering a period of 12 years are used to examine the trends in the water quality data in the HNRS. It has been found that there is an overall increasing trend of turbidity, chlorophyll-a, alkalinity, total iron, total aluminium, total manganese and reactive silicate, indicating an overall water quality deterioration in the HNRS during the last decade. The parameters such as phosphorus, suspended solids and ammonical nitrogen do not show any marked change over the period of study. Although an improvement in water quality can be seen at some stations downstream of the undisturbed parts of the catchment, there is a clear trend of increased chemical and physical water quality deterioration at many locations in the HNRS. Better land use planning i...

The provision of economic resources to countries at the shore of Gulf of Guinea is a very important effort that has helped West Africa to develop to some extent. Taking the study area as a system it will be important to know the dynamics that occur in it to help neighouring countries predict its physical and thermodynamics states at all times. Ghana is located in West Africa and it is bordered in the south by the Gulf of Guinea or Atlantic Ocean. The objective of this research is to characterize the hydrodynamic circulation of the Gulf of Guinea areas neighouring Ghana. A 3-Dimensional hydrodynamic model was implemented in the territorial waters of Ghana using the Modelo Hidrodinâmico, MOHID model to represent the dynamics and study the complex circulation pattern of the sea. To achieve this in an affordable computational time, nested domain approach was used to implement the hydrodynamic model in both 2 and 3-Dimensional gridded levels. The first level is a barotropic model with only tide. The nested domains of the rest of the levels are baroclinic forced with atmospheric and oceanic elements. To quantify its accuracy, the model was validated and calibrated in three stages; first, the frequency of the water level, followed by the circulation pattern and last, analysing the nature and profiles of the atmospheric and oceanic elements. The implemented model showed good agreement with the measured water surface level in the domain with mean error values not exceeding 14.00% of the measured data and with correlation factors higher than 0.80. Also, the intensity and direction of velocity observed in the current data are well represented by the model at the water surface levels with mean errors lower than 20.00% of the measured data components. The profiles obtained for both the temperature and salinity at shore show completely a straight line for salinity and also a straight line which is slightly curved at the top for the temperature profile. The vertical straight line for the salinity profile shows that at the shore the waters are not stratified vertically or they are well mixed. The slight curve at the top of the temperature graph accounts for the mixing dy

services are more generalizable than cultural services. Particularly in natural and semi-natural systems, spatial gradients need to be considered. We discuss uncertainties associated with this matrix-based EI and ES assessment approach and suggest that future large-scale studies should include additional land cover information and ecosystem disservices and may determine ES fluxes by differentiating between ES provision and consumption.

This study investigated the distribution of heavy metals in vegetative biofiltration columns irrigated by synthetic greywater. Twelve species of ornamental plants (three plants from each species) were planted in the same designed 36 biofiltration columns. Samples of effluent water, soils, roots, shoots and leaves were collected and analyzed. It was observed that before irrigation, the distribution of copper was in soils (0%), roots (42%), leaves (37%) and shoots (21%). After irrigation, this distribution changed to soils (29%), roots (39%), leaves (17%) and shoots (15%). It was found that lead concentrations decreased in soils from (84% to 7%), but increased in plants (from 16% to 93%) following irrigation with greywater. In contrast, the distribution of zinc changed from leaves (46%), roots (22%) and soils (16%) before irrigation to 89% in leaves and soils and 11% in shoots following irrigation. The chromium distribution before and after irrigation was found to be almost unchanged ...

This project report has been prepared as an output of the Australia-Indonesia Centre Small Project (Infrastructure Cluster),&lt;i&gt; A socio-technical investigation of Jakarta&#39;s opportunity for leapfrogging towards sustainable water management to identify common and distinct principles and practices&lt;/i&gt;.

The management of water resources in both irrigated and rain-fed agriculture is becoming an increasingly complex worldwide due to anticipated water scarcity, and compounded by the challenges of global warming and climate change. Climate-smart water technologies viz. Drip irrigation, Central pivot irrigation, Hydrogel and SWAT method need to be judiciously applied to overcome these challenges. Agriculture is a critical sector in India and other developing countries, providing substantial employment opportunities to rural populations and supporting efforts to achieve food and nutritional security. This paper addresses the challenge of increasing food production and improving rural livelihoods while safeguarding critical water resources for sustainable use, particularly in drought-prone regions, through adaptive measures for effective water management. An integrated approach is necessary for agricultural water management through the adoption of innovative technologies such as water har...