Morphology and ecological studies of the Chlorophyta in relation with water quality parameters of the Dor River Abbottabad, Pakistan

In this work, a hydrodynamic and water quality model was developed for Lake Nubia based on a two-dimensional, laterally averaged and finite difference hydrodynamic and water quality code, CE-QUAL-W2. The model was calibrated and verified using data which were measured in the years of 2006 and 2007 during low flood periods, respectively. Measurements during the flood season are not available. The results of the presented model show a good agreement with the observed hydrodynamic and water quality records.
\nTwo water quality indices (WQIs), NSF WQI and CCME WQI, have been developed to assess the state of water quality in the investigated case study, Lake Nubia, during the first low flood period of January 2006. The CCME WQI has been modified to use the Egyptian standards (objectives) of raw water. Moreover, another two trophic status indices, Carlson TSI and LAWA TI, have been developed to evaluate the trophic status of Lake Nubia during the same period of January 2006. Results of the previously developed hydrodynamic and water quality model for Lake Nubia were used to validate the model. According to the developed water quality indices results, Lake Nubia has a good water quality state during the low flood period. The modified CCME WQI (based on measured data) indicates that the Lake Nubia water quality state is excellent according to the Egyptian standards of water quality for surface waterways. Results of the applied trophic status indices show that the Lake Nubia trophic status is eutrophic during the studied period. 
\nThe effect of the global climate change on the hydrodynamic and water quality characteristics of Lake Nubia was conducted for the 21st century. To do that, the outputs of eleven global climate models for two global emissions scenarios combined with hydrological modeling were used. A theoretical process algorithm has been simplified, further developed and calibrated to modify the initial conditions of dissolved oxygen due to the global climate change effects. A sensitivity analysis has been conducted by using each of the predicted air temperature and inflow data separately in the model in order to investigate its effect on the characteristics of the hydrodynamic and water quality. Three hydrodynamic characteristics of the reservoir were investigated with respect to the climate change: water surface levels, evaporation water losses and thermal structure. In addition, eight water quality characteristics of the reservoir were investigated with respect to the climate change: dissolved oxygen, chlorophyll-a, ortho-phosphate, nitrate-nitrite, ammonium, total dissolved solids, total suspended solids and potential of hydrogen (pH). Moreover, the climate change effects on the water quality and trophic status indices have been studied. The results of the climate change study show partially significant impacts on the examined hydrodynamic and water quality characteristics, while the water quality and trophic status indices are slightly affected by the climate change scenarios.

This study performed a comprehensive assessment of water quality in four major tributaries - Yeongicheon, Samsungcheon, Bangchukcheon, and Jecheon - in Sejong Special Self-Governing City. The aim was to guide the development of effective river management policies and improve water quality for the first time. Monthly data from the city's water quality monitoring system, from January 2022 to June 2024, were analyzed. The study evaluated 11 water quality parameters. Additionally, we compared BOD and TOC, analyzed correlations between nutrient levels and organic pollutants, performed principal component analysis, and calculated a water quality index, and applied the Monthly Water Quality Index methodology. The results showed that the average water quality index ratings for all four rivers were classified as “Excellent,” aligning with trends observed in living environment standards. However, ratings derived from living environment standards showed greater variability than those from the water quality index, indicating the necessity for a combined water quality index to enhance river management strategies. Seasonal analysis revealed a decline in water quality during the rainy season, emphasizing the need for targeted management during this period. Among the tributaries, Yeongicheon, a rural stream, showed a greater need for nutrient management than the other urban streams, highlighting the importance of controlling non-point source pollution in its vicinity for comprehensive water quality improvement. The water quality demonstrated a tendency to decline during the rainy season, underscoring the necessity for enhanced water quality management during this period. Among the tributaries, Yeongicheon, a rural stream, exhibited a heightened requirement for nutrient management compared to the remaining three urban streams, indicating that effective control of non-point source pollution in the surrounding areas is essential for the comprehensive water quality management of these tributaries. In addition, the use of Monthly Water Quality Index developed in this study is recommended to calculate the water quality index with monthly water quality data.

Water quality index of springs in mid-hill of Nepal

Health of the Hooghly estuary was estimated through Water Quality Index (WQI) developed using ten water quality parameters, viz., specific conductivity, turbidity, pH, total dissolved solids, dissolved oxygen, biochemical oxygen demand, chloride, total alkalinity, total hardness, and nitrate-N measured at three different stations along the salinity gradient. Weighted arithmetic index method was used for calculation of WQI. For classifying water quality in each of the sampling stations, five-point rating scale was used. Wide fluctuations in monthly WQI were observed in this estuarine zone with the highest values during pre-monsoon month of May and the lowest values during monsoon month of October. From WQI values (mostly >100) throughout the year, it was evident that Hooghly-Bhagirathi estuarine system is unsuitable for drinking and propagation of wildlife and freshwater aqua culture as per water quality standards. It was also observed that there is a severe anthropogenic influence on most of the parameters. Major causes of deterioration in water quality were discharge of untreated industrial and municipal wastes, besides high level of other anthropogenic activities. Key words WQI; Hooghly estuary; India

Qanats in the aquifer of the Tehuacán Valley (Mexico) represent an ancient way of using groundwater that is still practiced today. They are used mainly for agricultural irrigation. However, anthropogenic activities have jeopardized the use of these aquifers. We analyzed 24 qanats in the Tehuacán Valley to assess water quality. Based on 24 physicochemical variables, a water quality index (WQI) was constructed on a zero-to-100 scale, divided into five water quality classes. A decision-tree analysis was applied to identify the parameters with the highest influence on the WQI, considering the water quality classes as categorical responses and the values of physicochemical variables as drivers of these categories. We produced interpolation maps to identify trends. The relationship between the WQI and the normalized difference indices of vegetation and salinity (NDVI and NDSI, respectively) was analyzed using a ternary diagram. WQI scores showed that 12.5% of the qanats have very good quality; 25%, good quality; and the remaining (62.5%) range from moderate to unacceptable quality. The CHAID classification-tree method correctly explained 83.3% of the categories, with sulfates, alkalinity, conductivity, and nitrates as the main parameters that explain water quality. WQI was inversely related to NDVI and NDSI, showing seasonal differences. Interpolation maps suggest a better water quality in the northern zone of the aquifer.

The usefulness of water quality indices, as the indicators of water pollution, for assessment of spatial-temporal changes and classification of river water qualities was verified. Four water quality indices were investigated: WQI (considering 18 water quality parameters), WQI(min) and WQI(m) (considering five water quality parameters: temperature, pH, DO, EC and TSS) and WQI(DO) (considering a single parameter, DO). The water quality indices WQI(min), WQI(m) and WQI(DO) could be of particular interest for the developing countries because of the minimum analytical cost involved. As a case study, water quality indices were used to evaluate spatial and temporal changes of the water quality in the Bagmati river basin (Nepal) for the study period 1999-2003. The results allowed us to determine the serious negative effects of the city urban activity on the river water quality. In the studied section of the river, the water quality index (WQI) was 71 units (classified as good) at the entry station and 47.6 units (classified as bad) at the outlet station. For the studied period, a significant decrease in water quality (mean WQI decrease = 11.6%, p = 0.042) was observed in the rural areas. A comparative analysis revealed that the urban water quality was significantly bad as compared with rural. The analysis enabled to classify the water quality stations into three groups: good water quality, medium water quality and bad water quality. WQI(min) resulted in overestimation of the water quality but with similar trend as with WQI and is useful for the periodic routine monitoring program. The correlation of WQI with WQI(min) and DO resulted two new indices WQI(m) and WQI(DO), respectively. The classification of waters based on WQI(m) and WQI(DO) coincided in 90 and 93% of the samples, respectively.

Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis

The article considered the ecological index of water quality, which is used for planning water protection activities, developing water protection measures, carrying out ecological and ecological and economic zoning, ecological mapping; water pollution index, which is determined by hydrochemical indicators; modified Horton water quality index models; the Water Quality Index is proposed by the Canadian Council of Ministers of the Environment; the Said index, which is used to assess the quality of water for general use; water quality index in the river subbasin in a certain year; The Nemerov Pollution Index, which is used to comprehensively assess water, precipitation, or soil quality, and the Oregon Water Quality Index. Advantages and disadvantages of water quality and pollution indices are determined.

Issue By 2025, half of the world's population will be living in water-stressed areas. Managing water quality and quantity is a worldwide concern that will require investing in WATER Centers and monitoring systems to improve the safety of drinking water and contribute to water conservation worldwide. Without reliable water policy climate change will threaten human survival. Problem There is no single measure that can describe overall water quality for any one body of water, let alone at a global level. Seven assessment methods used to measure water quality either on a national or global level, were reviewed and indexed. This index was examined and compared by objective, use, distribution and global location. Water centers need to review community water process, outcomes and outputs and also provide user populations with a Water Quality and Water Quantity Index (WQQI). Results United Nations Environment Programme UNEP research has revealed that “although there is no globally accepted composite index of water quality, some countries and regions have used, or are using, aggregated water quality data in the development of water quality indices.” 'Most water quality indices rely on normalizing, or standardizing data by parameter according to expected concentrations and some interpretation of 'good' versus 'bad' concentrations”. Lessons The survival of the human population requires policy changes regarding water management. The feedback humans need to survive can best be described as a Water Quality and Quantity Index (WQQI). With the growing scarcity of drinking water worldwide, proactive strategic thinking and planning is necessary. Message: Investing in WATER Centers ensures that the public health and economic benefits for all things related to water is optimized. The WQQI could also be useful in longitudinal and cross-sectional epidemiological studies. Key messages The survival of the human population requires policy changes regarding water management. Mankind needs real-time feedback about water quality to respond to threats to the water supply.

Evaluation of water quality and potential scaling of corrosion in the water supply using water quality and stability indices: A case study of Juja water distribution network, Kenya

Various pollutants have had a substantial impact on the quality of water in recent years. The quality of water directly impacts human health and the environment. The water quality index (WQI) is an indicator of effective water management. Water quality modelling and prediction have become essential in the fight against water pollution. The research aims to build an efficient prediction model for river water quality and to categorize the index value according to the water quality standards. The data has been collected from eleven sampling stations located in various locations across the Bhavani River, which flows through Kerala and Tamilnadu. The water quality index is determined by 27different parameters affecting water quality like dissolved oxygen, temperature, pH, alkalinity, hardness, chloride, coliform, etc. Data normalization and feature selection are done to construct the dataset to develop machine learning models. Machine learning algorithms such as linear regression, MLP regressor, support vector regressor and random forest has been employed to build a water quality prediction model. Support vector machines (SVM), naïve bayes, decision trees, MLP classifiers, have been used to develop a classification model for classifying water quality index. The experimental results revealed that the MLP regressor efficiently predicts the water Quality index with root mean squared error as 2.432, MLP classifier classifies the water quality index with 81% accuracy. The developed models show promising output concerning water quality index prediction and classification.

Drinking water quality describes the conditions for water to be accepted as suitable for human consumption. Water quality index is characterized by including in the process of assessment, water quality parameters such as physical, chemical and micro-biological. Nowadays, the management of hazardous events has become a big problem for water utilities and leads to the increase of the rate of public health risk. Availability of adequate management strategy in order to maintain good quality water has been always a challenge for water utilities. To provide a solution to the problem, one needs to think of a simple and effective tool that can be used to easily assess the quality of the water. Water Quality Index (WQI) and Water Stability Index (WSI) are known as the most reliable tools for assessing water quality and aggressiveness. This study aims at assessing the water quality and potential scaling and corrosion of the water supply in the Juja water distribution network by using WQI and WSI based on Langelier Saturation Index (LSI). Five sampling locations were selected including the treatment plant outlet and consumption points for physical, chemical and bacteriological water quality analysis and determination of WQI and WSI. It was found that 100% of the collected water samples had Calcium concentrations within the World Health Organization (WHO) and Kenya Bureau of Standards (KEBS) acceptable ranges. Additionally, 100% of the collected water samples had TDS concentrations within the WHO and KEBS acceptable ranges. However, at time, water quality parameters such as Residual Chlorine, E. coli, Alkalinity, and Turbidity were out of the WHO and KEBS standards. pH values ranged from 6.29 to 8.06 and was generally within acceptable limits. The WSI ranged between – 3.04 to – 0.99, indicating that the water is generally corrosive and may pose a risk to water quality and shorten the lifespan of the network facilities. Generally, the water from the Treatment Plant was of good quality, while at consumption points JKUAT Main Gate, JKUAT Campus, High Point, and Juja Stage had fair water quality based on the calculated WQI. There was a slight drop of the water quality associated with the low concentrations of residual chlorine, and the presence of E. coli at all the stations. The water quality therefore deteriorates in the distribution network and is corrosive throughout the system right from the treatment plant based on the WQI and WSI. The water company needs to improve on the treatment process, and relook at management strategies of the network, provide better services to consumers.

Australian ports serve diverse and extensive activities, such as shipping, tourism and fisheries, which may all impact the quality of port water. In this work water quality monitoring at different ports using a range of water quality evaluation indices was applied to assess the port water quality. Seawater samples at 30 stations in the year 2016–2017 from six ports in NSW, Australia, namely Port Jackson, Botany, Kembla, Newcastle, Yamba and Eden, were investigated to determine the physicochemical and biological variables that affect the port water quality. The large datasets obtained were designed to determine the Water Quality Index, Heavy metal Evaluation Index, Contamination Index and newly developed Environmental Water Quality Index. The study revealed medium water quality index and high and medium heavy metal evaluation index at three of the study ports and high contamination index in almost all study ports. Low level dissolved oxygen and higher level of total dissolved solids, turbidity, fecal coliforms, copper, iron, lead, zinc, manganese, cadmium and cobalt are mainly responsible for the poor water qualities of the port areas. Good water quality at the background samples indicated that various port activities are the likely cause for poor water quality inside the port area.

The Gökova Bay declared as Private Environment Protection Area (PEPA) in Turkey in 1989, where freshwater and seawater ecosystems are intertwined, is an important area that possesses high biodiversity. In the study, the water quality index (WQI), comprehensive pollution index (CPI) were used to determine the water quality and pollution of freshwater sources affecting the Gökova Bay. The water quality parameters were measured for each freshwater at both the dry and rainy seasons and all statistical analyses were carried out for each season, separately. Firstly, factor analysis was performed to determine the correlated water quality parameters and to collect correlated parameters under the common factors. The different factors were obtained from the dry and rainy seasons. This shows that the study area is fed by different water sources in the wet season and that the main factor affecting the area in the dry season is anthropogenic sources. The stations were statistically compared according to the factors. The relationships between the factors and WQI and CPI were investigated and lastly regression models between the factors and WQI and CPI were predicted. The results showed that the water quality parameters deteriorated in all the freshwater sources especially in dry seasons. A thematic map of each water quality and pollution index was developed in Geographic Information Systems (GIS). This study indicates that CPI can be used especially together with GIS as a useful tool for the evaluation of water quality and pollution.

Catfish is a freshwater fish that is widely cultivated but overfeeding causes organic pollution. This study aims to evaluate the water quality profile based on physicochemical and plankton as bioindicators in catfish ponds in Gondosuli Village, Tulungagung Regency. This type of research was ex post facto by monitoring the physicochemical parameters of water and the structure of the plankton community in control ponds, ponds with catfish aged <1 month, 2-3 months, and 3-4 months each with three replications. Water samples for each pond were measured on the physicochemical water quality included water temperature, conductivity, pH, water transparency, DO, BOD, and turbidity. Plankton identified and analyzed to determine community structure (Abundance, Frequency, Relative Abundance, Relative Frequency, Important Value Index) and biotic index (H', TDI, and %PTV) for water quality. The results of measurements of each physicochemical parameter between locations were statistically analyzed inferentially using a different test. The interaction between the plankton community structure and the physicochemical of water quality was analyzed using biplots. The results showed that ponds with the age of catfish ready to harvest had an impact on decreasing water quality. This condition was indicated by the high organic matter pollution reflected by the high BOD, high turbidity levels, and low DO values. Catfish pond waters quality based on the H' value of plankton community showed that organic matter pollution had not yet occurred. Based on TDI values, catfish pond waters were categorized as poor status (hyper eutrophic) and based on the %PTV index in ponds with catfish age 2-3 and 3-4 months were classified as high levels of organic matter pollution.