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The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R 2 values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R 2  > 0.90.

This study utilized MIKE 11 to quantify the spatio-temporal dynamics of water quality parameters (Biochemical Oxygen Demand (BOD5), Dissolved Oxygen (DO) and temperature) in the Long Xuyen Quadrangle area of the Vietnamese Mekong Delta. Calibrated for the year of 2019 and validated for the year of 2020, the developed model showed a significant agreement between the observed and simulated values of water quality parameters. Locations near to cage culture areas exhibited higher BOD5 values than sites close to pond/lagoon culture areas due to the effects of numerous point sources of pollution, including upstream wastewater and out-fluxes from residential and tourism activities in the surrounding areas, all of which had a direct impact on the quality of the surface water used for aquaculture. Moreover, as aquacultural effluents have intensified and dispersed over time, water quality in the surrounding water bodies has degraded. The findings suggest that the effective planning, assessment and management of rapidly expanding aquaculture sites should be improved, including more rigorous water quality monitoring, to ensure the long-term sustainable expansion and development of the aquacultural sector in the Long Xuyen Quadrangle in particular, and the Vietnamese Mekong Delta as a whole.

Industrialization, urbanisation and agricultural development cause pollution in water bodies due to the discharge of wastewater directly or indirectly. The present study aims to assess the water quality of water bodies in and around A D Nagar, Badharghat, Dukli and Budhjungnagar Industrial Estates, West Tripura during pre-monsoon and post-monsoon in the year 2016 to 2018. Biological parameters namely DO, BOD, Total Coliform, Faecal Coliform and COD, Physico-chemical parameters namely pH, EC, TDS, Bicarbonates Chlorides, Sulphates Total Hardness, Calcium, Magnesium etc. and heavy metals were analysed using standard methods as prescribed by APHA. The analysed parameters were compared with the standards prescribed by BIS. The BOD values for all the water bodies were beyond the prescribed standard limit except the pond located at the southern side of Jutemill, Hapania (S-3) during pre-monsoon season. The Total Coliform values for water bodies located at the eastern and northern side of Badharghat Industrial Estate were beyond the prescribed standard limit. The seasonal variations of water quality have also been observed. Water Quality Index values reflected that the 75% of surface water samples were of poor quality and 25% were of good quality in both the season. Correlation study revealed that positive and significant correlations between the pairs of selected parameters in surface water samples were observed. This study reveals that the surface water of these water bodies needs proper treatment before consumption and it also needs to be protected from the domestic as well as industrial contamination.

The inadequate availability of clean water presents systemic risks to human health, food production, energy generation and ecosystem functioning. Here we evaluate population exposure to current and future water scarcity (both excluding and including water quality) using a coupled global hydrological and surface water quality model. We find that 55% of the global population are currently exposed to clean water scarcity at least one month per year, compared with 47% considering water quantity aspects only. Exposure to clean water scarcity at least one month per year increases to 56–66% by the end of the century. Increases in future exposure are typically largest in developing countries—particularly in sub-Saharan Africa—driven by a combination of water quantity and quality aspects. Strong reductions in both anthropogenic water use and pollution are therefore necessary to minimize the impact of future clean water scarcity on humans and the environment. Polluted water contributes to water scarcity. Here the authors project water demands, availability and quality under climate and socio-economic changes and show that 56–66% of the global population will be exposed to clean water scarcity at the end of the century.

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO 3 -N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the “fair” status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land ( r  = 0.68, P  < 0.01) and a significant negative association with the built-up area ( r  =  − 0.94, P  < 0.01). To the best of the authors’ knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

In this study, observations were carried out in the surface waters of Saraydüzü Dam Lake within Sinop provincial borders for 1 year to determine water quality. The basic 28 variables used to determine water quality were measured monthly at six stations. Taking into account the World Health Organization's drinking water standards, the water quality index (WQI) and Turkey’s Ministry of Forestry and Water Affairs Surface Water Quality Regulations (SWQR) were used in determining the water quality. In addition, irrigation water quality was examined. For this, sodium absorption rates (SAR), sodium percentage and residual sodium carbonate (RSC) values were calculated. WQI values in the lake were found to be between 17.62 and 29.88. Water quality parameters did not exceed the recommended limit values in all months and at all stations. According to these values, the Saraydüzü Dam Lake water belongs to the ‘very good’ class in terms of drinking water quality. The results obtained showed that there were no nitrogen or phosphate inputs that could harm the ecosystem in the lake and that there were no low/insufficient ambient oxygen conditions resulting from excessive oxygen consumption during the degradation process of organic matter. All water quality parametres are well below the permissible limits except some heavy metals according to SWQR. Cu, Zn and Fe were found to exceed the limit values. The water quality of irrigation water was found to be good in terms of SAR and sodium percentage, whereas RSC was observed to have varying qualities during the year and not be suitable for irrigation in some months. According to results of factor analysis (FA), pH, temperature, electrical conductivity, suspended solid matter (SSM), biological oxygen demand (BOD), total hardness (TH),total alkalinity (TA), calcium, nitrate, ammonium, mercury and dissolved oxygen are the main variables responsible for the processes in the ecosystem.

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

In many parts of the world, groundwater is considered to be a key source of fresh water for both the domestic and non-domestic sectors. Where groundwater extraction is implemented, systems to monitor water quality must ensure a safe and sustainable supply. Over the years, Iraq has suffered from surface water quality and supply problems, necessitating groundwater extraction in many regions. This study investigates groundwater quality in a region of central Iraq around Babylon city, covering an area of 5119 km 2 . The data gathered for this study included maps, well locations and water quality data and was sourced from the relevant governmental departments. A base map of the focussed region was initially prepared following data collection. The analysed water quality parameters were used as an attribute database to produce thematic maps using a geographical information system (GIS) environment. In this paper, the water quality index (WQI) and the irrigation water quality index (IWQI) were calculated for different groundwater samples using various parameters including the Electrical Conductivity (EC), Cl − , HCO3 − , Na + and pH. Moreover, the groundwater suitability for irrigation purposes has been assessed using indices such as Kelly’s ratio (KR), sodium absorption ratio (SAR), residual sodium carbonate (RSC), soluble sodium percentage (SSP) and permeability index (PI). Water quality index maps have been developed using the GIS environment. The obtained results reveal that the groundwater in the study location requires specific treatments to be usable.

Chlorinated disinfectants are widely used in hospitals, COVID-19 quarantine facilities, households, institutes, and public areas to combat the spread of the novel coronavirus as they are effective against viruses on various surfaces. Medical facilities have enhanced their routine disinfection of indoors, premises, and in-house sewage. Besides questioning the efficiency of these compounds in combating coronavirus, the impacts of these excessive disinfection efforts have not been discussed anywhere. The impacts of chlorine-based disinfectants on both environment and human health are reviewed in this paper. Chlorine in molecular and in compound forms is known to pose many health hazards. Hypochlorite addition to soil can increase chlorine/chloride concentration, which can be fatal to plant species if exposed. When chlorine compounds reach the sewer/drainage system and are exposed to aqueous media such as wastewater, many disinfection by-products (DBPs) can be formed depending on the concentrations of natural organic matter, inorganics, and anthropogenic pollutants present. Chlorination of hospital wastewater can also produce toxic drug-derived disinfection by-products. Many DBPs are carcinogenic to humans, and some of them are cytotoxic, genotoxic, and mutagenic. DBPs can be harmful to the flora and fauna of the receiving water body and may have adverse effects on microorganisms and plankton present in these ecosystems.