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Monitoring the qualitative status of freshwaters is an important goal of the international community, as stated in the Sustainable Development Goal (SDGs) indicator 6.3.2 on good ambient water quality. Monitoring data are, however, lacking in many countries, allegedly because of capacity challenges of less-developed countries. So far, however, the relationship between human development and capacity challenges for water quality monitoring have not been analysed systematically. This hinders the implementation of fine-tuned capacity development programmes for water quality monitoring. Against this background, this study takes a global perspective in analysing the link between human development and the capacity challenges countries face in their national water quality monitoring programmes. The analysis is based on the latest data on the human development index and an international online survey amongst experts from science and practice. Results provide evidence of a negative relationship between human development and the capacity challenges to meet SDG 6.3.2 monitoring requirements. This negative relationship increases along the course of the monitoring process, from defining the enabling environment, choosing parameters for the collection of field data, to the analytics and analysis of five commonly used parameters (DO, EC, pH, TP and TN). Our assessment can be used to help practitioners improve technical capacity development activities and to identify and target investment in capacity development for monitoring.

Providing historical data on riparian plant biodiversity and physico-chemical parameters of stream water in Mediterranean mountains helps to assess the effects of climate change and other human stressors on these sensitive and critical ecosystems. This database collects data from the main natural headwater streams of the Sierra Nevada (southeastern Spain), a high mountain (up to 3479 m above sea level [m asl]) recognized as a biodiversity super hotspot in the Mediterranean basin. On this mountain, rivers and landscapes depend on snowmelt water, representing an excellent scenario for evaluating global change’s impacts. This dataset covers firstto third-order headwater streams at 41 sites from 832 to 1997 m asl, collected from December 2006 to July 2007. Our goal is to supply information on the vegetation associated with streambanks, the essential physico-chemical parameters of stream water, and the physiographic features of the subwatersheds. Riparian vegetation data correspond to six plots sampled at each site, including total canopy, individual number, height and DBH (diameter at breast height) in woody species, and cover percentage for herbs. Physico-chemical parameters were measured in situ (electric conductivity, pH, dissolved O₂ concentration, stream discharge) and determined in the laboratory (alkalinity, soluble reactive phosphate-phosphorus [SRP], total phosphorus [TP], nitrate-nitrogen [NO⁻ ₃ –N], ammonium-nitrogen [NH⁺ ₄ –N], total nitrogen [TN]). Watershed physiographic variables comprise drainage area, minimum altitude, maximum altitude, mean slope, orientation, stream order, stream length, and land cover surface percentage. We recorded 197 plant taxa (67 species, 28 subspecies, and 2 hybrids), representing 8.4% of the Sierra Nevada vascular flora. Due to the botanical nomenclature used, the database can be linked to FloraSNevada database, contributing to Sierra Nevada (Spain) as a laboratory of global processes. This data set can be freely used for non-commercial purposes. Users of these data should cite this data paper in any publications resulting from its use.

Reuse of treated wastewater for irrigation purpose can reduce high pressure on freshwater resources. A horizontal subsurface flow constructed wetland (HSSF CW) system filled with gravel and planted with Phragmites Australia was used to treat the real wastewater at Al- Rustumia wastewater treatment plant. Some characteristics of wastewater such as biochemical oxygen demand, phosphate and total suspended solids have been monitored from 15 January until 8 July 2018. The results proved that HSSF unit has a good efficacy in the reduction of previous parameters with removal of 84.2, 55.4 and 72.7% while sulphate and total dissolved solids were less removal efficiency with 3.3 and 0.99 % respectively. The measured values of these parameters were within the permissible limits suitable for irrigation purposes.

In this paper, the quality of a source of drinking water is assessed by measuring eight water quality (WQ) parameters using 710 samples collected from a water-stressed region of India, Jodhpur Rajasthan. The entire sample was divided into ten groups representing different geographic locations. Using American Public Health Association (APHA) specified methodology, eight WQ parameters, viz., pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), calcium hardness (Ca-H), residual chlorine, nitrate (as NO3−), and chloride (Cl−), were selected for describing the water quality for potability use. The quality of each parameter is examined as a function of the zone. Taking the average parametric values of different zones, a unique number was used to describe the overall quality of water. It was found that the average value of each parameter varies significantly with zones. Further, we used neural network (NN) modeling to map the nonlinear relationship between the above eight parametric inputs and the water quality index as the output. It can be observed that the NN designed in the present work acquired sufficient learning and can be satisfactorily used to predict the relational pattern between the input and the output. It can further be observed that the water quality index (WQI) from this work is highly efficient for a successful assessment of water quality in the study area. The major challenge to uniquely describing the drinking water quality lies in understanding the cumulative effect of various parameters affecting the quality of water; the quantified figure is subjected to debate, and this paper addresses the difficulty through a novel approach. The framework presented in this work can be automated with appropriate equipment and shall help government agencies understand changing water quality for better management.

Constructed wetlands (CWs) are simple low-cost wastewater treatment units that use natural process to improve the effluent water quality and make it possible for its reuse.in this study used the horizontal flow system for the tertiary treatment of wastewater effluent from secondary basins at Al-Rustamiya wastewater treatment plant / old project / Baghdad / Iraq. the Phragmites Australis plant was used for wastewater treatment and the horizontal subsurface flow system was applied. the experimental study was carried out in February 2020 to October 2020. the parameters were monitored for a period of five weeks, Concentration-based average removal efficiencies for HSSF-CW were COD,53% [NO]_3,41.3% [PO]_4,52% and TSS, 54.2%. Thus, constructed wetland can be considered as a sustainable alternative to the tertiary conventional treatment of wastewater, thus making it possible for reuse..

Biochar (BC) is a well-established physical treatment method. The high-cost BC limits their use as adsorbents in wastewater. Thus, deriving BC from cheap and locally available waste materials is needed to develop a feasible waste removal technology. Nowadays, BC technology makes it possible to envision a new strategy to manage invasive plants by converting them into value-added products like BC. Hence, the present study was designed to evaluate the potential utilization of BC as an efficient filter medium made by invasive aquatic plants, Salvinia spp., and Eichhornia spp. A mass of 50 g of prepared activated and nonactivated BC was incorporated in a sand and gravel filter to treat rubber-manufactured wastewater. Wastewater was passed through the filter, and both raw and treated water samples were analyzed for pH, Total Suspended Solids (TSS), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN), Ammoniacal-Nitrogen (NH3-N), Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Phosphates (TP), Nitrate (NO3-N), turbidity and heavy metals (Zinc, Chromium). The control filter was developed only with sand and gravel, excluding BC. Fourier Transform-Infrared Spectroscopy (FT-IR) and Scanning electron microscopy (SEM) were used to analyze BC’s chemical and physical characteristics. A brine shrimp lethality assay was carried out for toxicological evaluation. OH stretching (3,550-3,200 cm−1), C=C aromatic stretching (1400-1660 cm−1), and Phenol-O-H bending (1,300-1,400 cm−1) were recorded in all BC samples that involved the adsorption mechanism. Observed images indicated differences in surface morphology of both activated and nonactivated BC were observed under SEM observation. The study concludes that the filter unit incorporated with activated Eichhornia spp. Gave the best treatment efficiency when compared to filter units incorporated with other activated and nonactivated BC. The toxicity assay revealed 100% mortality in the control setup and raw wastewater but only 60–70% in the nonactivated BC integrated filters. Activated BC-incorporated filters showed no mortalities. Hence, the study’s outcomes suggest a green approach using invasive aquatic plants for sustainable wastewater treatment.

Drinking water quality for human consumption is a global matter of paramount importance. The study aimed to assess the physicochemical and bacteriological quality of drinking water from five major sources in Sapele, Delta State. Using a convenient sampling method, 40 water samples were collected from river, rain, well, borehole, and sachet water sources and examined for physicochemical and bacteriological characteristics. The pH of the water sources examined ranged from 4.5 to 6.8, the total dissolved solids (TDS) ranged between 5 and 14,000 mg/l, the electrical conductivity (EC) ranged between 10 and 740 μS/cm, and the turbidity ranged between 0.01 and 23.9 NTU. Mean levels of chloride, calcium, iron, lead, copper, and cadmium were below the maximal permissible ceilings based on WHO and NSDWQ standards. The total coliform count ranged between 0 and 9,000 MN/100 ml, with the mean concentration ranging between 0.001 and 1,268.13 MPN/100 ml. Water samples from different sources had physicochemical parameters within the stipulated standards, but the biological parameters revealed water sources with contamination. It is recommended that consumers of water from these different sources employ measures to purify their drinking water to forestall potential health risks.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model.

The study assessed the status of water quality parameters for an urban water body (Hatirjheel Lake) in Dhaka, the Capital city of Bangladesh. Nine different water samples were collected from nine points of the lake during the dry season in January 2021. Water quality parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), total suspended solids (TSS), total alkalinity, total acidity, total hardness, Ca2+ hardness, free CO2, and dissolved oxygen (DO) were determined for the samples. The status of the parameters is pH (6.51-7.05), EC (510-600 μS.cm-1), TDS (450-590 ppm), TSS (0.0-0.034 mg.L-1), total alkalinity (80-392 mg.L-1), total acidity (224-500 mg.L-1), total hardness (348-452 mg.L-1), Ca2+ hardness (74-162 mg.L-1), free CO2 (730-1170 mg.L-1), DO (2.7-5.5 mg.L-1). However, the DO value at some points of the lake is too less (2.7 mg.L-1 and 3.7 mg.L-1) than the standard value (> 5-6 mg.L-1) of ECR, DoE, which might not be healthy for any water body and aquatic ecosystem. Other water quality parameters are within the permissible limit of WHO and ECR, DoE.