ЕКОЛОГІЧНА ХАРАКТЕРИСТИКА ВОДНИХ ОБ’ЄКТІВ ГІРНИЧОДОБУВНИХ РЕГІОНІВ (НА ПРИКЛАДІ РІЧКИ ІРША ЖИТОМИРСЬКОЇ ОБЛАСТІ)

The study is devoted to the development of scientifically grounded recommendations for integrating the requirements of the European Union Water Framework Directive (WFD) and River Basin Management Plans (RBMPs) into the process of calculating the maximum allowable discharges of pollutants from wastewater using current national methodologies. The Danube River basin is considered as an example based on monitoring data from 2015-2024. Special attention is paid to the assessment and forecasting of water quality, which are key elements of the water quality regulation procedure. The study analyzes the water legislation of the European Union countries regarding the assessment of surface water quality. According to the provisions of the WFD and at the initial stage of RBMPs, the water quality in a water body over a certain past period is considered compliant with EU standards if the probability of exceeding the normative values (assurance level) for each indicator does not exceed a specified limit, F. Based on this, it is proposed to carry out regulation, as well as the associated assessment and forecasting of surface water quality, using indicators with assurance level F. This approach ensures alignment of national methodologies for these calculations with EU legislative requirements. The proposed provisions were tested using the analysis of temporal variability of water quality indicators in the Danube river. It was shown that when using average values of indicators, the WFD and RBMP requirements are often not met because the assurance level of these values can significantly exceed the established limits for the frequency of MAC exceedances. To gradually achieve “good status” of surface water bodies according to the WFD and RBMPs, it is advisable to use values of indicators with assurance levels of 10%, 5%, and 2.5% as the design (background) values when regulating pollutant discharges from wastewater. In addition, when temporal trends of indicators are present, their values should be forecasted to ensure compliance with the WFD requirements, especially in the case of positive trends.

Environmental assessment of water is an integral part of monitoring water bodies. It provides information about water as a component of the aquatic ecosystem, the habitat of hydrobionts and an essential part of the natural environment, which is necessary for establishing environmental standards for water quality and adoption of measures to improve the ecological state of water bodies. Assessment of the quality of the ecological environment is an important parameter of sustainable development, necessary for the rational use of nature. Currently, the Zaporizhzhia (Dnipro) reservoir is exposed to increased anthropogenic impact, which affects water quality. The ecological state of the reservoir is deteriorating, and mass algal bloom is observed. The work aims to provide an integrated environmental assessment of water quality in different sites of the Zaporizhzhia reservoir. The study was conducted in 2019 at 5 sites along the course of the Zaporizke reservoir, which differ in hydrological and hydrochemical conditions: Samara Bay, Festivalnyi pier, Monastyrsky island, creek of the Mokra Sura river and the lower section of the reservoir (near Viyskove village), following generally accepted hydrobiological methods. The article calculates block indices and provides an environmental assessment of the water quality of the Zaporizhzhia reservoir according to the "Methodology for environmental assessment of surface water quality in the corresponding categories. The assessment of water quality by the criteria of contamination with salt composition components indicates that the water body condition is good at most of the studied points. The worst indicators were recorded in Samara Bay, where increased mineralization is observed for a long time. The trophic-saprobological block revealed to have the worst indicators and a high amplitude of index fluctuations. No significant deviations were observed in terms of the content of toxic and radiation exposure indicators throughout the entire studied water area, which makes it possible to assign water to the same class. It has been determined that the water quality of the Zaporizhzhia reservoir according to the average annual data is characterized as "good" in terms of the state and "clean, fairly clean" in terms of purity degree. However, during the study period, significant deviations from the average were observed, especially in the summer. There is a need for systematic monitoring of the reservoir, the establishment of annual dynamics, which will make it possible to determine the causes of deteriorating water quality and develop ways to address them.

The relevance of the work consists in the need to study the water quality of the Siverskyi Donets River and its tributaries at the beginning of the 21th century, since the river is a main source of water supply in the eastern part of Ukraine. The catchment area of the Siverskyi Donets River is located in the most industrialized region of Ukraine. It is a transboundary river and its catchment is located in the territory of both Ukraine and Russia. The volume of the river's water use, if compared to its runoff volume, is the largest in Ukraine. The condition of water quality is determined by high water consumption and large amount of polluted water being discharged into it. The largest impact is caused by discharges from the residential and commercial complex (big cities) and the enterprises of coal industry, ferrous metallurgy, chemical and petrochemical industry which are concentrated in the Donets Coal Basin.
 The purpose of this work is to assess the changes of surface water quality of the Siverskyi Donets Basin and to establish the role of the tributaries affected by considerable anthropogenic pressure in the formation of the ecological state of the main river.
 The research aims at analyzing the hydrochemical indices of the Siverskyi Donets River and its tributaries: the Udy River (9 km to the south from Kharkiv) located in the forest-steppe zone and affected by the wastewater of Kharkiv megalopolis; the Oskil River (Chervonooskilske Reservoir) located in the karst area, having a significant underground water supply and the runoff of which is regulated by the reservoir; the Luhan, Kryvyi Torets, Kazennyi Torets, Bakhmut rivers that are tributaries originating from the Donetsk Highlands and affected by the wastewater of Donbas industrial and municipal enterprises.
 The subject of the research is the ecological condition of water resources in the rivers of the Siverskyi Donets Basin estimated using the generalized ecological index.
 The water quality condition was analyzed based on the hydrochemical observations performed from 1990 to 2015 by Central Geophysical Observatory in Kyiv. The main method of research is the method of ecological assessment of surface water quality of land and estuaries in Ukraine using relevant categories.
 The assessment of the surface water quality using the generalized ecological index (with application of average indicators) showed that the water quality deteriorates along the main river with its worst condition at Lysychansk section. Analysis of the tributaries indicated the worst ecological condition of the Bakhmut River (Donbas). For most of the considered rivers the 3rd class of quality (“satisfactory”), the 4th category ("satisfactory condition and slightly contaminated”) were established. If we consider the index of ecological condition by maximum indicators, the results are almost the same for all the studied rivers: 5th class, 7th category ("very poor condition and very contaminated”). According to the block of salt composition components, the worst ecological condition is established for sections of the Siverskyi Donets River (city of Lysychansk) and the Bakhmut River. The analysis of the generalized ecological index dynamics along the length of the main river indicated a general trend (except for the section in Lysychansk) to water quality improvement. This was achieved following the decrease of toxic effects due to reduction of petroleum products and phenol concentrations. No significant changes in the index were detected for the tributaries. Donbas rivers are also characterized by increase of the salt composition components index: their water is classified as "brackish" whereas the water of other tributaries is classified as “fresh water”. The pollution with nitrogen compounds still remains significant and keeps increasing. The heavy metals content decreases very slowly. The inflow of water of the Donbas rivers to the main river causes significant changes of the ecological condition at the Lysychansk section located downstream from the place of their confluence.

Ramganga River is the main tributary of the Ganges River which is the most sacred and largest river basin of India. For effective management of Ganges, assessment of water quality in its tributaries is must, and this river lacks it so far. The present study focuses on the evaluation of water quality of this river and its adjoining tributaries. Organic pollution indicators, chemical oxygen demand (COD) and biological oxygen demand (BOD5) of river water ranges from 15.2 to 55.5 mg/L and 7.1 to 29 mg/L, respectively. Nutrient parameters nitrate (NO3−-N) and phosphate (PO42−-P) of river water ranges from 0.2 to 12.7 mg/L and 0.02 to 0.76 mg/L, respectively. While in tributaries, these parameters range from 0.2 to 9.9 mg/L and 0.03 to 1.47 mg/L, respectively. The most polluted stretches of river were from Moradabad to Farrukhabad via Bareilly especially in terms of organic pollution. Pair sample t test applied to compare the water quality of river and its tributaries revealed no significant difference in COD, NO3−-N, PO42—P, and fluoride (F−) while sulfate (SO42−) was significantly large (25.1 mg/L) in tributaries. The spatial variation in water quality of river was addressed by cluster analysis (CA) which grouped the 16 sampling points into three significant clusters corresponding to lower pollution, moderate pollution, and severe pollution regions. The results from CA restructure the entire sampling campaign to a cheaper and less-effort sampling program that will be helpful in water quality assessment and management of the river.

This study focuses on the assessment of water quality at seven sampling points along the Pazundaung Creek in Yangon. Moreover, the determination of BOD, COD, DO, TH, EC, Cu, Cl, TDS, pH, turbidity and temperature were carried out. Water samples had been collected in February, March, April and May. According to the findings of this study, the water quality of Pazundaung Creek was deteriorated due to the rapid urbanization and industrialization along this creek. Therefore, the sources of these types of wastewater should be carefully controlled by applying the effective waste management systems and appropriate wastewater treatment technologies to prevent the water pollution of this creek. All of the results from sampling analyse were compared with Malaysia Water Quality Standard of class IIA and class IIB. According to the standard limitations of Malaysia Water Quality Standard, this type of surface water needed a conventional treatment process to use as water supply. In the case of fishery, the sensitive aquatic species can survival in these limitations and can also be used for recreational use with body contact.

Radiological assessment of surface water quality around proposed uranium mining site in India

Assessment of surface water quality using Fuzzy Analytic Hierarchy Process (FAHP): A case study of Piyain River's sand and gravel quarry mining area in Jaflong, Sylhet

Assessment of water quality is imperative because clean water is essential for human use and for balanced ecosystem. In view of continuous pollution of water bodies due to point and non-point sources, it is required to protect and manage freshwater resources. Various monitoring program and modelling approaches have been developed and suggested in past for effective management. In modelling the surface water body is studied for an extended period of duration and is experienced as tedious and complex task with enormous amount of quality parameter data. Therefore, water quality index (WQI) has been used extensively as a tool to access the river water quality. It can help decision makers and stake holders to analyze the water quality easily. Water quality assessment has been performed on many Indian rivers in terms of WQI and the suitability of water resource for human consumption has been addressed. The proposed chapter will provide a compressive review of water quality of Indian rivers and can provide an effective approach for assessment and management of water quality.KeywordsWater quality index (WQI)MonitoringHealth riskAssessmentManagement

The study attempted to assess the water quality around the tannery effluent discharge areas for suitability for irrigation purposes using different indices (Water Quality Index (WQI)) and statistical analysis in Savar Upazilas, Dhaka, Bangladesh. The samples were collected three times, from monsoon 2021 to pre-monsoon 2023. The analysis results showed that the concentrations of various parameters at most of the sampling points exceeded the surface water standard. Pollution levels were found in the following order: pre-monsoon, post-monsoon, and monsoon. At SW1 point, the CWQI, WWQI, and MWQI of the surface water quality showed a ‘poor’ category. The IWQI values showed that the surface water at all the sampling points was suitable for irrigation purposes, except for the SW1 sampling point. The NPI indicates that EC, BOD, COD, TSS, Cl−, Na+, and NO3--N parameters were potentially responsible for polluting most sampling sites. Multivariate statistical analyses like principal component analysis, cluster analysis, and Pearson correlation matrix showed significant anthropogenic intrusions of these variables in surface water in the area. A strong correlation between these parameters indicated their common origin, i.e., poorly treated tannery industry effluent entered the surface water, suggesting an improvement in the efficiency of the Central Effluent Treatment Plant (CETP).

Water pollution by potentially toxic elements has become a growing global concern, particularly in developing countries, where rapid industrialization and mining activities often outpace environmental regulation. In Brazil, mining stands out as one of the main contributors to surface water contamination, especially in historically exploited regions such as the Iron Quadrangle. This study presents a comprehensive assessment of surface water quality in the Iron Quadrangle, one of Brazil's most important mining regions, with a focus on contamination by potentially toxic elements. A total of 487 water samples were collected from third-order drainage basins across an area of 7,000 km², resulting in a high sampling density (one point per 14.4 km²). Samples were analyzed for major, minor, and trace elements and compared with national and international water quality guidelines. Pollution levels were evaluated using the Heavy Metal Pollution Index (HPI) and the Heavy Metal Evaluation Index (HEI), complemented by high-resolution geochemical mapping. The results revealed elevated concentrations of As, Cd, Pb, Cr, and Zn, with numerous samples exceeding drinking-water standards. The highest concentrations were observed in the Doce, das Velhas, and Paraopeba river basins, particularly within the municipalities of Nova Lima, Mariana, Ouro Preto, and Brumadinho. HPI values ranged from 0.9 to 2871, with 34% of the samples classified as highly polluted. Arsenic, Pb, and Cd were the dominant contributors to HPI, with mean values of 36.8, 35.4, and 26.8, respectively, far exceeding those of other elements (0.001–2.83). Pb and As alone exceeded the pollution threshold (HPI > 100) in 14.3% and 14.1% of the sampling points, respectively.HEI values ranged from 3.2 to 70.6, with a mean of 8.65. Overall, 22% of samples were classified as moderately polluted and 6.8% as polluted. As, Pb, and Cd again dominated HEI contributions, with average values of 2.95, 2.61, and 1.25, markedly higher than those of other elements (0.016–0.87). The comparison of the indices indicates that HPI exhibits greater variability due to its element-specific weighting, whereas HEI shows a more stable, uniform behavior. The spatial distribution of the indices highlighted severely polluted areas associated with intense mining activity, unplanned urbanization, and natural geogenic sources. The spatial patterns of both indices delineate severely contaminated zones linked to intensive mining, unplanned urbanization, and geogenic inputs. The integrated methodology proved effective in identifying critical contamination hotspots near urban areas, rural communities, and water supply intakes, offering a robust scientific basis for environmental management, monitoring programs, and public policy development in highly impacted regions.

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.

Surface water quality assessment is critical for environmental protection and public health management, yet traditional methods are often time-consuming and costly, limiting their application for real-time monitoring. Machine learning (ML) approaches offer promising alternatives for automated water quality assessment and understanding of key influencing factors. This study employed six ML algorithms to predict water quality grades using comprehensive data from China’s national surface water monitoring network. A dataset comprising 79,015 water quality measurements collected from 1 January to 14 February 2025 was processed with nine physicochemical parameters as input features. The XGBoost model demonstrated superior predictive performance with 99.04% accuracy. Feature importance analysis revealed that nutrient-related parameters (total phosphorus, permanganate index, ammonia nitrogen) consistently ranked as the most critical factors across all models. SHAP analysis provided interpretable explanations of model predictions, revealing grade-specific discrimination patterns where excellent quality waters are primarily distinguished by phosphorus limitation, while severely polluted waters require multi-parameter approaches. This study demonstrates the effectiveness of ML approaches for large-scale water quality assessment and provides a scientific foundation for optimizing monitoring strategies and environmental management decisions in China’s surface water systems.

This paper presents a study on assessment of water quality. According to a study, mining activities have a significant impact on water quality (lakes, surface water and groundwater), which has become a major problem globally. Due to mining and exploitation processes, lakes can be formed around these mines. Also, these lakes have been formed around the world and are steadily increasing. The purpose of this study is to watch the quality of water from the area around mining activities. This study refers to the, Groapa Burlacu lake around the mining exploitation Targu Ocna, Romania. This lake was formed on the northern bottle of the massive salt, strongly affected by the underground activities. Sampling for the determination the concentrations of Cl- and NaCl from the studied area was made at different depths (0 m, -5 m, -10 m, -15 m, -20 m, -25 m, -30 m, -35 m -40 m). Besides these concentrations, physical parameters of the water (pH, turbidity, electrical conductivity, dissolved oxygen and temperature) were also measured. To determine the physical parameters in the monitored area, sampling was done from four different points of the area and then put together for analysis. These parameters were measured on site using portable equipment. The data on the analyzed concentrations indicate that at depths of less than 5.0 m, the NaCl concentration values are more than 250 g/L. Keywords: mining activities, water quality, Cl-, NaCl, physical parameters

Surface water pollution driven by land use practices and soil erosion remains a persistent challenge in tropical river basins of East Africa. Despite its socio-economic importance, the transboundary Sio Malaba Malakisi River Basin (SMMRB), shared by Kenya and Uganda, lacks comprehensive data on spatial and seasonal water quality dynamics. This study provides the first year-long baseline assessment of surface water quality in the SMMRB, using water samples collected from 12 monitoring sites across three distinct hydrological seasons: dry, short rainy, and long rainy. Twelve physicochemical parameters were analyzed following standardized protocols from the American Public Health Association (APHA), resulting in 854 data points. Multivariate statistical techniques: agglomerative hierarchical clustering, Wilk’s lambda analysis, and exploratory factor analysis (EFA), were used to identify patterns and key drivers of water quality variation. Three distinct spatial clusters, corresponding to the Sio, Malaba, and Malakisi sub-catchments, were identified, each exhibiting unique water quality profiles. Elevated concentrations of total phosphorus (TP), soluble reactive phosphorus (SRP), and total suspended solids (TSS) were observed, exceeding typical background levels for unpolluted rivers. Seasonal differences highlighted the role of sediment transport and dilution processes, particularly during the rainy seasons. These findings provide novel insights into nutrient transport and hydrogeomorphological influences in a tropical, data-scarce, transboundary basin. The results offer a scientific basis for setting up targeted monitoring stations and adaptive water management strategies. Future studies should assess long-term interactions between sediment and nutrients. Evaluating the effectiveness of soil and water conservation practices will also be important for improving water quality.

Multivariate statistical techniques, such as principal component analysis (PCA) , cluster analysis (CA) were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of the lakes in Wuhan, generated in 2009 monitoring of 21 parameters at 70 different lakes (1470 observations) located at the 7 core districts of Wuhan, Hubei Province, China. Results reveal that Potassium Permanganate Index, Biochemical oxygen demand (BOD), Ammonical nitrogen NH4-N, Total Phosphate TP, Total nitrogen, Chl-a were the parameters that are the most important ones in assessing variations of water quality in the lake. Hierarchical cluster analysis grouped 70 lakes into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) lakes, based on the similarity of water quality characteristics. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective lakes water quality management.This study suggests that PCA and CA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.