Factors Influencing Water Quality Research Articles

Assessing the Effects of Environmental Flows on Water Quality for Urban Supply

This paper analyses the effects of environmental flows on water quality within a highly regulated basin, focusing on the Turia River basin in the eastern Iberian Peninsula. Through water management and water quality models, a series of simulations were conducted, introducing variations in the outflows of the Loriguilla reservoir to evaluate the effects of different environmental flow scenarios on water quality, particularly at the location of the intake for the water supply to Valencia. Three environmental flow scenarios were analyzed, alongside an alternative management scenario, considering their implications on water quality and reliability of water demand. The findings of this paper, particularly the nitrate (NO3−) concentration evolution, highlight the influence of minimum e-flow and e-flow regimes on water quality within the basin. These results suggest that while modifying the current flow regime can lead to some improvements in nitrate concentrations at the Valencia supply intake point, the primary cause of high nitrate concentrations is attributed to irrigation return flow and the pre-existing contamination of the aquifer. This analysis offers valuable insights into the complexities of water quality management in regulated basins, emphasizing the need for a multi-faceted approach to address the diverse factors influencing water quality and demand supply reliability.

Water quality assessment using Bi-LSTM and computational fluid dynamics (CFD) techniques

Assessment of water quality is imperative for informed decision-making, ecological preservation, and sustainable water resource management, particularly in light of escalating anthropogenic pollution. Traditional approaches often face complexities and inaccuracies in forecasting, necessitating advancements in modelling techniques. Machine learning methods, particularly deep learning, present promising avenues for accurate water quality estimation, albeit with challenges. This study aims to enhance water quality forecasting through the integration of deep learning, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks, with computational fluid dynamics (CFD). The objective is to address the limitations of existing methods by leveraging the synergies between deep learning and fluid dynamics simulations, thereby improving predictive accuracy and efficiency. By proposing a novel framework that combines Bi-LSTM networks with computational fluid mechanics, this study introduces an innovative approach to water quality assessment. The integration of these techniques offers a comprehensive solution to overcome the complexities inherent in traditional methods, promising greater reliability and efficiency in predicting water quality parameters. The study employs Bi-LSTM networks alongside CFD simulations to model contaminant movement and diffusion in water bodies, particularly focusing on the Yamuna River. Utilizing empirical data on Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD), the framework aims to predict key factors influencing water quality. The ANSYS software, operating within a Windows environment, facilitates the execution of the proposed methodology. Evaluation of the proposed model demonstrates superior performance compared to traditional methods, exhibiting higher predictive reliability and lower margins of error. Through a comprehensive comparison study, the Bi-LSTM and CFD-based approach surpasses existing techniques in accurately forecasting water quality parameters, thus highlighting its efficacy and potential for practical application. The integration of Bi-LSTM networks with computational fluid dynamics presents a promising avenue for enhancing water quality evaluation. The study underscores the significance of leveraging advanced computational techniques to address the challenges associated with forecasting water quality, ultimately contributing to informed decision-making and sustainable water resource management.

Integrating multivariate hydrogeochemical analysis with human health risk assessment: An inverse geochemical and statistical modeling approach

Chandigarh, an urban center meticulously planned in India, partially relies on groundwater for its water supply. However, rapid urbanization, geological heterogeneity, and anthropogenic actions have elevated the significance of ensuring water quality. This research employs advanced methodologies including inverse geochemical modeling through PHREEQC, multivariate statistics, and the Nemerow Pollution Index (NPI) to discern the governing factors influencing water quality. Furthermore, a human health risk assessment evaluates the potential non-carcinogenic impacts of NO3− and F− on human health. Results indicate during PRM (pre-monsoon), 90 % of shallow aquifer samples exceed an NPI value of 1.0, denoting water impurities. In POM (post-monsoon), 70 % of both shallow and deep groundwater samples surpass this threshold. Approximately 70 % of PRM deep groundwater samples and 75 % in POM exhibit “hard water” characteristics. Hydrogeochemistry is primarily influenced by rock-water interactions, corroborated by Gibb's diagram and negative values of chloro-alkaline indices (CAI-I and CAI-II) indicating cation exchange reactions. Piper's plot illustrates a prevailing Ca-Mg-HCO3 hydrochemical facies, with oversaturation of calcite, dolomite, aragonite, and fluorite at varying depths. Saturation indices (SI) affirm calcite oversaturation, implying CaCO3 precipitation and CaF2− dissolution. Additionally, 33 % of PRM and 52.4 % of POM shallow groundwater samples indicate carbonate weathering's influence on mineral content. Principal Component Analysis (PCA) and Pearson's correlation demonstrate contributions from irrigation return flows and anthropogenic activities alongside geogenic factors, enriching Na+, SO42−, NO3−, and Cl− concentrations. The combined statistical and geochemical models offer a comprehensive insight into groundwater geochemistry which will help the policy makers and municipality to manage groundwater resources effectively in the area.

Improving the efficiency of water use in agriculture by modelling the classification of groundwater quality

This study investigates the potential of machine learning for classifying groundwater quality in Telangana, India, to optimize water resource utilization in agriculture. The study aims to develop and evaluate a decision tree model capable of accurately predicting groundwater quality based on chemical composition data. The objective is to identify key factors influencing water quality and provide insights for improving water management practices and enhancing agricultural productivity. The study utilizes a dataset of groundwater quality parameters collected over three years (2018-2020) and employs a decision tree algorithm for model development. The results demonstrate the effectiveness of the model, achieving an accuracy of 95.7%. The analysis highlights the significance of sodium content, dissolved salts ratio, total dissolved solids, and total water hardness as key factors influencing groundwater quality. This research underscores the potential of machine learning for enhancing water resource management in agriculture and suggests further exploration of temporal dynamics, predictive modeling, and broader geographic application to further refine and extend the model’s impact.

Hydrochemical characterization of the natural Tamda Lake and its environs (Middle Atlas, Morocco)

The Middle Atlas region, distinguished by its diverse natural landscapes, hosts approximately forty lakes, each formed through unique geological processes, predominantly tectonic movements and karst phenomena. This research focuses on a specific case study of a lake formed by a landslide within the northern Middle Atlas. Employing both field and laboratory water analysis techniques, this study meticulously evaluates the physico-chemical parameters of the lake's water and its adjacent aquatic systems. The primary objective is to delineate the physico-chemical quality of the water in Tamda Lake and to elucidate its hydrological attributes, thereby contributing to the broader understanding of limnological dynamics in geologically diverse terrains. This investigation not only sheds light on the physico-chemical status and hydrological behavior of Tamda Lake but also enhances our comprehension of lake formation processes in the Middle Atlas, offering insights into the environmental and geological factors influencing water quality in naturally occurring lakes.

Water quality assessment of Kuwano River, Basti (U.P.) India, with reference to statistical analysis

The present study analyses water quality parameters in the Kuwano River, Basti district, using correlation and regression analysis to establish relationships between variables and provide a comprehensive understanding of the factors influencing water quality. Kuwano is the main river that flows through Basti city. The water samples were collected at three locations. The values of different physicochemical parameters of the river water sample were found to be dependent on the hydrology of the area. The pH was strongly associated with TDS (r = 0.885), DO (r = 0.744), COD (r = 0.969), TH (r = 0.806), and Mg (r = 0.944). The biological oxygen demand (BOD) (-0.345), nitrate (-0.235), and calcium (-0.128) exhibited an inverse correlation with total dissolved solids (TDS), whereas nitrate and calcium had a positive correlation with all other physicochemical parameters. The mean TDS value of the river water sample (81.2) was within the permissible limit for drinking water. The total coliform counts established a negative correlation with most of the parameters studied, e.g., dissolved oxygen (-0.628), BOD (-0.983), chemical oxygen demand (-0.194), total hardness (-0.549), nitrate (-0.955), Ca (-0.918) and Mg (-0.279). The study's findings may provide practical information for decision making in river pollution management.

Predictive Modeling of WQI of Trans Amadi Industrial Layout, Port Harcourt, Rivers State Nigeria Using Multiple Linear Regression

It is important to monitor water quality and understand factors influencing indices like the Water Quality Index (WQI). This study aimed to assess water quality and to develop a predictive model for WQI based on common ions and using multiple linear regression analysis. Water samples were collected from 15 locations in Trans Amadi Industrial Layout, Port Harcourt and analyzed for 19 groundwater quality parameters (Temp, EC, DO, Turbidity, NO3, HCO3, pH, TDS, CI, SO₄, Fe, Na, Ca, Mg, Zn, Cu, Cr, Pb and Cd). Results of correlation analysis revealed that there were significant relationships between some of these parameters and the WQI. A multiple linear regression (MLR) model was developed to quantitatively examine relationships between the Water Quality Index (WQI) and key indicator variables and to predict the WQI based on the parameters. The model exhibited a strong coefficient of determination (R-squared = 0.9811), indicating a high level of accuracy in predicting the WQI. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values confirmed the model's predictive capability. Overall, this study contributed to understanding the factors influencing water quality and showed that linear regression is a reliable method for predicting WQI. The model accurately explains variation in WQI based on nitrate, chloride, sodium, calcium and magnesium levels.

Nitrogen and Phosphorus Loading Characteristics of Agricultural Non-Point Sources in the Tuojiang River Basin

Agricultural non-point source (ANPS) pollution has emerged as a significant factor influencing water quality within watersheds. Understanding the spatial distribution and composition of ANPS is crucial for effective river water quality management. Based on the statistical data of 28 districts and counties in the Tuojiang River Basin (TJRB), the load distribution characteristics of total nitrogen (TN) and total phosphorus (TP) from ANPS were studied in this work by using the pollutant discharge coefficient method. In 2018, ANPS contributed 60,888.92 tons of TN and 20,085.98 tons of TP to the TJRB. By 2019, the TN load had decreased to 57,155.44 tons, while the TP load increased to 21,659.91 tons. Spatially, TN and TP loads follow a pattern of being lowest in the upstream, intermediate in the downstream, and highest in the middle reaches. Planting sources emerged as the primary contributors to TN and TP loads from ANPS in the TJRB, accounting for 61.43% and 77.39%, respectively. Rural living sources made a lesser contribution, at 20.23% for TN and 9.15% for TP, while poultry and livestock farming sources accounted for 18.34% of TN and 13.46% of TP loads. The analysis of grey water footprint (GWF) and water pollution level (WPL) revealed that TN and TP loads continued to exert significant pressure on the TJRB’s water environment throughout the study period. These findings offer valuable insights for enhancing water quality management in the TJRB.

Comparative Analysis of Water Quality Parameters in Major Rivers of Southeast Asian Cities: A Literature Review"

The purpose of this research is to provide a complete understanding of the river water quality state in key Southeast Asian cities and determine the factors influencing water quality. A thorough search of electronic databases such as Scopus, Web of Science, and Google Scholar was conducted. A checklist derived from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria was used to assess the quality of the selected studies. The study included 18 studies that examined several parameters such as pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and fecal coliform (FC). The findings suggest that water quality in Southeast Asian cities' major rivers is generally bad, with notable exceptions.

Inversion of Nutrient Concentrations Using Machine Learning and Influencing Factors in Minjiang River

Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution multispectral imagery combined with machine learning techniques is an effective solution for this difficulty. Three machine learning methods based on support vector regression (SVR), neural network (NN) and random forest (RF) were used to invert TN and TP using actual water-quality measurement data and Sentine-2 remote-sensing images, and analyzed the factors influencing water quality in terms of pollutant emissions and land use. The results show that RF performs the best in both TN (R2 = 0.800, RMSE = 0.640, MSE = 0.400, MAE = 0.480) and TP (R2 = 0.830, RMSE = 0.033, MSE = 0.001, MAE = 0.022) inversion models, and that the optimal selection of feature variables improves model performance. The TN and TP concentrations in the Minjiang River Meishan Water Function Development Zone were the highest in the downstream section and in 2018. Analysis of the factors influencing water quality shows that pollution sources and amounts were closely related to land-use types, and land use in riparian zones at different spatial scales had different degrees of impact on water quality.

Assessment of the spatiotemporal water quality variations in the Middle Route of China's South-to-North Water Diversion Project by multivariate analysis.

As an important drinking water source for North China, the Middle Route of China's South-to-North Water Diversion Project (MRP) must provide high-quality water to maintain the health and safety of more than 60 million people. However, different water transfer operation modes may affect the water quality status, and the spatiotemporal characteristics of water quality in the MRP, with high water transfer volumes, remain poorly understood. In this study, the differences in water quality in the MRP between the initial stage (Nov. 2015 to Oct. 2017, low transfer volumes) and the current stage (Nov. 2017 to Oct. 2020, high transfer volumes) were compared, and the spatiotemporal water quality variations in the current stage were evaluated using multivariate statistical methods. For this purpose, approximately 12,528 observations, including the datasets of 12 water quality parameters collected from 29 monitoring sites, were used. The results showed that the water quality status improved significantly during the current stage. Based on principal component analysis (PCA), physical parameters (natural), nutrients, organic matter and microbes (anthropogenic), and heavy metals (natural and anthropogenic) were the key factors influencing water quality variations. Based on hierarchical cluster analysis, 12months were classified into two groups: the high-flow period (HFP, Jun.-Oct.) and the low-flow period (LFP, Nov.-May). Additionally, 29 sampling sites were grouped into three sections: the Henan section (HN; S1-S16), Hebei section (HB; S17-S24), and Tianjin-Beijing section (TB; S25-S29). From the perspective of water quality regulation, the total nitrogen concentration and permanganate index in the HB and TB sections of the MRP should be considered throughout the year, and the faecal coliform concentrations in these two sections should also be considered during the HFP. The results of this study could be helpful for local administrations to understand and control pollution and better protect the quality of water in the MRP.

Influence of Hydraulic Model Complexity on Results of Water Age and Quality Simulation in Municipal Water Supply Systems

The age of water in the municipal water supply system is one of the main factors influencing water quality. To create a good quality hydraulic model, one must achieve a high level of calibration accuracy with real life measurement data. Before we start building our model, we must decide on the model’s level of detail, that is, its complexity. We must know if skeletonization of the network graph and different hydraulic timesteps have an influence on simulation results. This study strives to prove that this decision can lead to unforeseen problems during the calibration process, thus making it impossible to achieve the required calibration precision. In order to prove this, two different model variants were created with different levels of graph detail, and simulation data results were used to determine which model variant is best suited to achieve the highest fidelity simulation results. Following this, the chosen model was run with different hydraulic timestep settings, which made it possible to showcase the large influence this setting has on achieved results.

Effect of multiple spatial scale characterization of land use on water quality.

Land use in uplands is an important factor affecting water quality in its respective catchment, and its influences at the different spatial scales and configurations warrant further investigation. Here, we selected 26 catchments in the upper Han River (China) and sampled the surface water at the outlet of each catchment in four seasons during 2019. Multivariate statistics were used to identify the relationships between land use characteristics in uplands and water quality in river system. The results indicated that chemical oxygen demand (CODMn); pH; dissolved oxygen; electrical conductivity; nutrient, i.e., NH4+-N, NO3--N; and dissolved phosphorus (DP) in rivers displayed significant seasonal variations. Stepwise regression revealed that landscape metrics such as patch density, landscape shape index, and splitting index were important factors influencing water quality in rivers regardless of their spatiality and seasonality. Urban was the most frequently chosen land-use type in the best prediction models, and forest area showed a negative correlation with water quality parameters in most cases for example, DP. Overall, the influence of land use on river water quality was slightly stronger at reach scale than at catchment and riparian scales. Also, nutrients (i.e., NH4+-N, NO3--N, and DP) in rivers were primarily impacted by the land use characteristic at catchment and riparian scales. Our results suggested that multi-scale explorations would help to achieve a fully understanding on the impacts of land use on river water quality.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET′) reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Revealing factors influencing spatial variation in the quantity and quality of rural domestic sewage discharge across China

Understanding the characteristics of domestic sewage discharge in different rural areas is an important prerequisite for effective sewage treatment. Here, we revealed the spatial variation in the water quantity and quality of rural domestic sewage and its influencing factors in 31 provinces and cities of China, based on a multi-factorial analysis. The results showed that the water quantity of rural domestic sewage discharge generally increased from Northwest to Southeast in China and the water quality had opposite pattern. The water quantity was positively correlated with water resources, gross domestic product, rainfall, and temperature, but negatively correlated with altitude. Water resources, with an influence weight of 25.04% by principal component analysis, had the greatest impact on the spatial variation in water quantity. Differently, gross domestic product, which was directly negatively correlated with ammonium-nitrogen and total phosphorus in structural equation model, was a dominant factor influencing water quality. These findings will help us to better understand the factors of controlling rural domestic sewage discharge at a national scale and to optimize the treatment technology of rural domestic sewage.

Impacts of land use and landscape pattern on water quality at multiple spatial scales in a subtropical large river

AbstractThe coupling between land use/landscape pattern and water quality in river systems varies across different spatial and temporal scales. It is important to understand the association between water quality and land use/landscape pattern across different spatial and temporal scales for the protection of water resources. Here, we measured seasonal water quality at 12 sub‐basins in the upper reaches of the Han River (UHR) between 2010 and 2018. We conducted factor analysis and redundancy analysis to determine the links between land use and water quality at multiple spatial scales and to identify the main factors influencing water quality. We found that the concentration of nutrients, including total nitrogen, total phosphorus, nitrate‐N and ammonium‐N, was higher during the wet season than the dry season. Total nitrogen was the main driver of nutrient pollution in the UHR, whereas total phosphorus was identified as another potential nutrient pollutant. We also found that water quality parameters were more strongly related to land use types during the wet season than the dry season. Croplands and urban lands increased phosphorus concentrations of river water, whereas forest and grasslands decreased the nitrogen concentrations of river water at the sub‐basin scale. Land use at the riparian zone scales better explained variations in water quality than land use at sub‐basin scales. The explained variations in landscape metrics were generally higher during the dry season than the wet season. The largest patch index and Shannon's diversity index were the main predictors of river water quality in the UHR.

Dynamic impacts of changes in river structure and connectivity on water quality under urbanization in the Yangtze River Delta plain

The deterioration of river water quality in urbanized areas is getting more serious, and affects the regional ecology and development of social economy; however, its mechanisms of dynamic variation is still an open question. In this study, we detected the dynamics of water quality and its driven mechanisms in the Yangtze River Delta plain, one of the most developed areas in China. The results showed that the spatial agglomerations of dissolved oxygen (DO), ammonia nitrogen (NH3-N), and total phosphorus (TP) presented seasonal differences, which exhibited a trend of shifting from west to east and then to west from spring to winter. Further, the relative contribution rates of the river network characteristics affecting water quality were quantified on the basis of backpropagation artificial neural networks. We found that the average contribution rates of river structure (more than 60%) were higher than that of river connectivity, and the dominant factors influencing water quality were water surface ratio (WP) and multifractal indices (Δa, Δf). Specifically, the average relative contribution rates of WP, Δa and Δf were 18.72%, 15.03%, and 14.52% during the flood season, respectively, and 15.83%, 16.58%, and 14.54% during the non-flood season. The functional connectivity influenced by obstruction of sluices also influences water quality, which accounting for 11.15% and 12.85% in the flood and non-flood seasons, respectively.

Assessing the factors influencing water quality using environment water quality index and partial least squares structural equation model in the Ebinur Lake Watershed, Xinjiang, China.

Surface water quality deterioration is commonly associated with environmental changes and human activities. Although some research has been carried out to evaluate the relationship between various influencing factors and water quality, there is still very little scientific understanding on how to accurately define the key factors of water quality deterioration. This study aims to quantify the impact of environmental factors and land use land cover (LULC) changes on water quality in the Ebinur Lake Watershed, Xinjiang, China. A total of 20 water parameters were used to calculate the Environment Water Quality Index (CWQI). Meanwhile, the partial least squares-structural equation model (PLS-SEM) was used to quantify the impact of eleven factors influencing water quality in the watershed. About 33.3% of the monitoring points that located mostly in the downstream region with dominant anthropogenic activities were detected as poor quality. There were no obvious temporal changes in water quality from 2016 to 2019. The PLS-SEM simulation shows that the latent variable "land use/cover types" (path coefficient = - 0.600) and "Environmental factor" (path coefficient = - 0.313) are two major factors affected water quality in the Ebinur Lake Watershed, with a strong explanatory power to water quality change (R2 = 0.727). In the latent variable "Environmental factors", the "NDVI" and "night light brightness value" have a great influence on water quality, with the weights of 0.451 and 0.427, respectively. Correspondingly, the "farmland" and "forest land" within the latent variable of "Land use/cover type" have a considerable impact water quality, with the weights of 0.361 and - 0.340, respectively. In conclusion, the influence of anthropogenic activities on surface water quality of the Ebinur Lake Watershed is greater than that of environmental factors. Compared with the traditional multivariate statistical method, PLS-SEM provides a new insight for quantifying the complex relationship between different influencing factors and water quality.

Assessment of Water Quality Target Attainment and Influencing Factors Using the Multivariate Log-Linear Model in the Nakdong River Basin, Republic of Korea

Because identifying the factors affecting water quality is challenging, water quality assessment of an individual component based on the arithmetic mean method cannot adequately support management policies. Therefore, in this study, we assessed the water quality target attainment at 24 sites in the Nakdong River Basin by applying multivariate log-linear models to identify factors influencing water quality, including flow and season. The temporal and seasonal water quality trend and flow were also analyzed using the calculated model coefficients. Specifically, weekly data on biological oxygen demand (BOD), total phosphorous (TP), and flow during 2013–2018 were used to investigate the 2018 water quality target attainment level for this river. The significance and suitability of the models were analyzed using the F-test, root mean squared error (RMSE), mean absolute percent error (MAPE), and adjusted R2 values. All 24 models applied in this study showed statistical significance and suitability for the prediction of BOD and TP concentrations. Moreover, flow was identified as the main factor affecting water quality and had a predominant effect on BOD and TP concentrations in tributaries and the main stream, respectively. Furthermore, among the 24 sites, BOD and TP targets were evidently attained at 18 and 17 sites, respectively.

CYCLES OF SOLAR ACTIVITY AS A BASIS FOR FORECASTING THE QUALITY OF DNIEPER WATER

One of the main features of the quality of the Dnieper water is the content of natural organic substances which determine the water oxidizability and color indexes. A special problem is that organic substances are poorly removed by water treatment plants. A significant problem in the operation of water treatment plants is the unpredictability of annual changes in the quality of Dnieper water, which complicates logistics management, for example, the supply of reagents. Many years of experience related to the Dnieper water supply station in Kyiv (DnWS) have led to the hypothesis that these changes are in some way related to the cycles of solar activity.
 The purpose of this work is to test this hypothesis to substantiate the possibility of predicting the quality of Dnieper water depending on the action of such a global factor as solar activity. The work was performed by statistical methods using 60-year results of Dnieper water analyzes provided by the DnWS laboratory, as well as Wolf numbers (Sunspot Numbers) provided for free use by the Royal Observatory of Belgium. The influence of solar activity on the quality indicators of Dnieper water, especially in its peak and post-peak periods, has been statistically confirmed. The interaction of biological and physicochemical parameters as factors influencing water quality is shown.
 Conclusions. As a result of the construction of six reservoirs in 1931-1976, the Dnieper from the river actually turned into a network of water bodies. Now these reservoirs are a kind of reactors in which water is treated by electromagnetic solar radiation, which leads to the cyclic transformation of phytoplankton and natural organic matter. Taking into account the cycles of solar activity, the logistical planning of the operation of water treatment facilities receives an independent "base", changes in which can be predicted with some reliability for years to come.