Water Quality (Engineering)

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.

Water quality monitoring is crucial for safeguarding human health, maintaining ecosystems, and protecting aquatic life. As concerns about water pollution escalate and the demand for real-time monitoring grows, traditional manual methods for assessing water quality are increasingly seen as inadequate. This paper explores the development of an innovative IoT-based water quality monitoring system, designed to deliver real-time data by integrating sensors for key water quality parameters, including pH, temperature, and water level. Central to the system is an Arduino microcontroller, which processes the sensor inputs and provides real-time insights into water quality

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.

A trunk sewer interceptor plus a treatment plant and a disposal system are the main components of most large wastewater management infrastructures. Wastewater processing starts in the sewer is continued in the treatment plant and generally ends after disposal of final products (sludge and treated effluent). Long-sea outfalls can be the most efficient disposal systems in many coastal areas because they (i) promote dilution of the effluent into receiving waters during plume rising, (ii) transport wastewater off the coastline and (iii) can discharge the water in areas with low residence time. In this paper Costa do Estoril Waste Water Management System is described. The impact of the discharge on the water column and sediments is very light and only detectable in the vicinity of the diffuser. Modeling has shown that the low impact in the water column is a consequence of the hydrodynamic and biogeochemical characteristics of the receiving waters. The small impact in sediments (e.g. the absence of the "piling effect") is a consequence of the wave climate, which resuspends fine material, making it available to be transported by tidal and wind currents. Monitoring results and model predictions have been used to demonstrate that a secondary treatment would mean no environmental benefit for the receiving waters, an essential condition to obtain a license for operating with a primary treatment out of the bathing period. This is the optimal exploitation configuration that complies with the European Directive on urban wastewater treatment. This paper presents a synthesis of the studies supporting the demand for operating with a primary treatment out of the bathing period.

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.

Dwg 1. DD2-Site plan Dwg 2. DD2-Wall section Dwg 03. Prefinal-Site plan Dwg 04. Prefinal-Individual building plans and section Dwg 05. Prefinal-Individual building plans and section Dwg 06. Prefinal-Individual building plans and section Dwg 07. Site plan Dwg 08. Temple waste management center-Ground floor plan Dwg 09. Temple waste management center-First floor plan Dwg 10. Temple waste management center-Second floor plan Dwg 11. Temple waste management center-West elevation Dwg 12. Temple waste management center-North elevation Dwg 13. Cultural center-Section Dwg 14. Temple waste management center-Section Dwg 15. Cultural center-Ground floor plan Dwg 16. Cultural center-First-floor plan Dwg 17. Cultural center-Second-floor plan Dwg 18. Pilgrim accommodation-Ground floor plan Dwg 19. Pilgrim accommodation-Typical floor plan Dwg 20.

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.

We designed a simple, portable, low-cost and low-weight nondispersive infrared (NDIR) spectroscopy-based system for continuous remote sensing of atmospheric methane (CH 4) with rapidly pulsed near-infrared light emitting diodes (NIR LED) at 1.65 μm. The use of a microcontroller with a field programmable gate array (μC-FPGA) enables on-the-fly and wireless streaming and processing of large data streams (~2 Gbit/s). The investigated NIR LED detection system offers favourable limits of detection (LOD) of 300 ppm (± 5%) CH 4 ,. All the generated raw data were processed automatically on-the-fly in the μC-FPGA and transferred wirelessly via a network connection. The sensing device was deployed for the portable sensing of atmospheric CH 4 at a local landfill, resulting in quantified concentrations within the sampling area (ca 400 m 2) in the range of 0.5%-3.35% CH 4. This NIR LED-based sensor system offers a simple low-cost solution for continuous real-time, quantitative, and direct measurement of CH 4 concentrations in indoor and outdoor environments, yet with the flexibility provided by the custom programmable software. It possesses future potential for remote monitoring of gases directly from mobile platforms such as smartphones and unmanned aerial vehicles (UAV).

Streams contribution on bathing water quality after rainfall events in Costa do Estoril-a tool to implement an alert system for the bathing water quality, SI 56 (Proceedings of the 10th International Coastal Symposium), pg-pg. Lisbon, Portugal, ISBN Bathing waters of Costa do Estoril are located near urban and rural areas, crossed by streams discharging into the sea/estuary. In order to evaluate the contribution of these streams on bathing water quality after rainfall events, a monitoring program financed by SANEST, SA was established. The final aim of this work is to be able to provide bathing water quality information from the automatic monitoring of water levels in streams. To achieve this goal, the relationship between water level, flow and contamination must be determined. The monitoring program included sampling of water quality indicators (e.g faecal coliforms) in streams and beaches, water level and discharge measurements in streams and the implementation of models capable of explaining local trends, and ultimately forecast hydrodynamics and water quality. Also, automatic hydrometric stations were installed in Marianas and Sassoeiros streams. These stations automatically measure water level and send real-time alerts to a central workstation when the water level rises above predetermined values. In addition, currents along the beaches were measured using acoustic profilers. These data provide a better comprehension of local hydrodynamics and information to validate models. Results show correlation between water level and flow that allow estimating stream outflow from water level. Numerical predictions were made using MOHID Water Modelling System. Simulations using measured stream discharge show that the model can accurately simulate dispersion and evolution of the streams plume and bathing water quality.

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.

Constructed wetlands (CWs) are man made vegetation filter systems, which simulate the ability of natural wetlands to remove pollutants from water. Eco-technical treatment can facilitate the re-use of process waters, drainage waters and effluents. Aquatic microbial communities, in association with plant roots and a supporting mineral matrix, are effective at removing pollutants, such as suspended solids, dissolved and particulate organic matter, nitrogen, phosphorus, metals and pathogenic organisms from effluent streams. Treatment of polluted waters in a small, self-contained CW bed is relatively inexpensive and involves low technology with a "green" image. CWs can enable contaminated waters to be re-used productively, for example in agriculture, horticulture and energy forestry. Applications for CWs include wastewater (sewage) treatment at secondary or tertiary stages, sludge drying, surface runoff (commercial, industrial), groundwater treatment, industrial and agricultural process water treatment, and for ecological habitat creation. CW systems vary in design, including surface flow vegetated channels and sub-surface soil/vegetation filters. The latter may employ horizontal flow (HF), vertical flow (VF) or tidal flow (TF) hydraulic regimes and these may be combined in hybrid systems to optimise pollutant removal. Macrophyte species planted in the bed include reeds (Phragmites), cattails (Typha), rushes (Juncus) and willow (Salix). The porosity of the bed fill material is critical for the hydraulic loading rate and retention time of effluent passing through it, which in turn determines the efficiency of water treatment. The microbial community in the bed is responsible for the processes of degradation and chemical transformation, which result in pollutant removal. Both aerobic and anaerobic processes are involved, but degradation of carbonaceous matter (BOD) to CO 2 and transformation of ammonia to nitrate require biological oxidation, and hence a continual supply of oxygen. This is achieved most efficiently in compact VF systems; as the surface is flooded, air is forced into the bed, while effluent percolates downwards through the matrix. Horizontal flow (HF) beds typically achieve lower oxygen transfer rates but, with largely anaerobic conditions, they are effective in removing nitrogen to atmosphere via de-nitrification. The role of plant roots in providing an oxygen source in the subsurface environment, according to the Root-Zone Model, and in creating reduced/ oxidised micro-environments in the matrix, is considered. Evidence for the release of oxygen by plant roots and the presence of aerobic/anaerobic micro-gradients is discussed, with reference to measurements in laboratory microcosms and in-situ field systems. The importance of plant uptake in removing N and P from effluent is assessed. CWs are effective in treating polluted waters arising from a wide range of domestic, industrial and agricultural operations and are particularly appropriate for isolated rural situations, enabling water of acceptable quality to be discharged to environment, or to be re-used locally as fertilizer. CW technology offers a cost-effective means of protecting water resources from contamination, whilst providing local habitat diversification.

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 ...

In this study, 9 remaining water components of Madian River in the Baraftab were considered. Autoregressive Integrated Moving Average modeling techniques are considered appropriate in creating and predicting components. SO42-, Na + and SAR show a declining fashion despite the different properties of brilliant water, which show an increasing lightness. However, practicing a delay to the position of the style desk was sure that the time group was ready for painting. Similarly, the time group study suggests that there is an unfamiliar location increase mode for all components except SO42-, Na + , and SAR. EC, Cl-, Ca2 + , Mg2 + and HCO3-show an increasing style, which is suggested to reduce the prominent water within the area. Depend on the sphere revisions, the extreme successful and relative population density, rising the use of artificial frameworks, departure city effluents and mainstream of rural manure in conservative method via streams, inopportune plans of burial litters, dispersal of nonsenses and disorders in floor waters and watercourses which later influx via streams are taken into deliberation because the important reasons of water brilliant worsening. Agronomic effluents and livestock are different causes that pollute floor water. Also, the water hazard outstanding worsening is rising because of extreme public boom with inside the area and green changes are energetic with inside the zone to exclude you larger ecological devastation.

In this study, 9 remaining water components of Madian River in the Baraftab were considered. Autoregressive Integrated Moving Average modeling techniques are considered appropriate in creating and predicting components. SO42–, Na + and SAR show a declining fashion despite the different properties of brilliant water, which show an increasing lightness. However, practicing a delay to the position of the style desk was sure that the time group was ready for painting. Similarly, the time group study suggests that there is an unfamiliar location increase mode for all components except SO42–, Na + , and SAR. EC, Cl–, Ca2 + , Mg2 + and HCO3– show an increasing style, which is suggested to reduce the prominent water within the area. Depend on the sphere revisions, the extreme successful and relative population density, rising the use of artificial frameworks, departure city effluents and mainstream of rural manure in conservative method via streams, inopportune plans of burial litters, disp...

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.

Water is an important commodity that is becoming scarce due to man-made and natural destruction. The Philippines has abundant water resources but the rapid development, anthropogenic activities, and poor policy implementation in the country leads to the deterioration of water quality in its water bodies. Water quality monitoring (WQM) is a tedious and costly process that can impinge countries with limited resources. On the other hand, emerging technologies on wireless sensor network, energy harvesting, remote sensing, and geographic information system (GIS) have been the focus of various researches in order to achieve a precise, periodic, real-time, and low-cost water quality monitoring. In this work, these enabling technologies are reviewed to evaluate their applicability in the assessment, management, and rehabilitation of contaminated water bodies in the Philippine context. To carry out this study, the water quality monitoring program, existing technologies being employed, and th...

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;.

A pressure piped irrigation system is a network installation consisting of pipes, fittings and other devices properly designed and installed to supply water under pressure from the source of the water to the irrigable area.  Water sources  Energy sources (Gravity,Mechanical, Internal combustion) and  distribution network (pipe or canal) are basic irrigation components. Introduction cont'd COMPONENTS DEFINITION Introduction (cont's) A pressurized irrigation system operates through a piping system where water flows under pressure as compared to gravity systems where water flows due to level difference.The pressurized irrigation system provides opportunities of minimum water losses which otherwise occur due to seepage, surface absorption and through evaporation in gravity flow condition particularly in open channel flow. NETWORK LAYOUT  There are many kinds of pressurized irrigation systems. However, a thorough examination of the various system layouts, the equipment and the principles in operation shows that the same approach is always employed from the planning procedure to their application and that all of them have most of their features and components parts in common.  In all piped systems the main component parts are:  The control station (head control unit);  The mains and submains (pipelines);  The hydrants;  The manifolds (feeder pipelines);  The laterals (irrigating pipelines) with the emitters. PUMPING STATION MAJOR COMPONENTS  The building  The hydraulic system: the pump and related piping  The electrical system: the motor and its related components  The control system: pressure, flow and level switches Centrifugal Pumps  A centrifugal pump is a rotodynamic pump that uses a rotating impeller to increase the pressure of a fluid. Centrifugal pump with an electrical motor Centrifugal Pumps (cont'd)  A centrifugal pump is one of the simplest pieces of equipment in any process plant.  Its purpose is to convert energy of a prime mover (a electric motor or turbine) first into velocity or kinetic energy and then into pressure energy of a fluid that is being pumped.  The energy changes occur by virtue of two main parts of the pump, the impeller and the volute or diffuser. Centrifugal Pumps (cont'd)  The impeller is the rotating part that converts driver energy into the kinetic energy.  The volute or diffuser is the stationary part that converts the kinetic energy into pressure energy.  All of the forms of energy involved in a liquid flow system are expressed in terms of feet of liquid i.e. head.  The total head energy in a pump system is the sum of potential head energy, static pressure head energy and velocity head energy.  This machine consists of an IMPELLER rotating within a case (diffuser)  Liquid directed into the center of the rotating impeller is picked up by the impeller's vanes and accelerated to a higher velocity by the rotation of the impeller and discharged by centrifugal force into the case (diffuser).

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 ...

A regional study on the arsenic concentration in surface waters and stream sediments, with a density of one sample every 13 km2, was carried out for the first time in the Quadrilátero Ferrífero (Brazil). The region was divided into 3rd order catchment basins, in which 512 areas were sampled. The arsenic concentration was determined in waters and stream sediments after partial digestion with the aid of ICP-OES. The arsenic values found in surface waters ranged from 57.70 to 414 µg.L-1, while for stream sediments, arsenic concentrations ranged from 0.63 to 1691 mg.kg-1, and from the 512 sampling points, 135 (26%) had arsenic concentrations above the limit of detection, which was 0.63 mg.kg-1. It was also found that 106 3rd order catchment basins had values above the third quartile, (5.09 mg.kg-1). The results show that high concentrations of this element are strongly related to the presence of Nova Lima rocks that contain minerals rich in arsenic. However, the anthropogenic influence ...

Solar disinfection (SODIS) is an economical and user-friendly method of water purification mostly applied in the tropical countries. SODIS uses the ultraviolet (UV) and temperature effects of the sun to kill pathogens in drinking water. The objective of the study was to develop a functional relationship between pathogen die-off rate (k e) and SODIS treatment conditions, namely daily maximum of 5-h averages of UV intensity (UV 5), daily maximum water temperature (T m), and water turbidity (Tu). Regression analyses/models based on 5-month data revealed that although UV 5 (R 2 ¼ 0:84) and T m (R 2 ¼ 0:87) can be excellent predictors of pathogen die-off rate, their variable inflation factors (VIFs) (6.75 and 6.57, respectively), which is a measure of dependence between the two variables and adequacy of the estimated regression coefficients, suggest that they cannot be successfully combined in a single least-square regression model. T m was found to be a better predictor of k e in SODIS than UV 5. Water turbidities in the range of 1-30 NTU had no significant effect on the pathogen die-off rate. The study concluded that regression models/analyses can be successfully employed to explain the day-today variability in pathogen die-off rate, predict SODIS applicability, and exposure period in different regions of the world based on UV, water temperature, and water turbidity.

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.