Estimate Water Quality Research Articles

A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation

A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation

A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation

Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.

Evaluation of the Effect of Channel Geometry on Streamflow and Water Quality Modeling and Modification of Channel Geometry Module in SWAT: A Case Study of the Andong Dam Watershed

The impact of the channel geometry on water quantity and quality simulation of the Soil and Water Assessment Tool (SWAT) was evaluated for the Andong Dam watershed. The new equations to determine the bankfull width of the channels and the bottom width of the floodplains were developed using aerial photographs, and its performance was compared with the current equations of SWAT. The new equations were more exact than the current equations since the current equations tended to overestimate the widths of the channel and floodplain. When compared with the observed data, the streamflow of the scenario 2 (S2, applying the new equations) showed lower deviation and higher accuracy than scenario 1 (S1, applying the current equations) because the peak flow of S2 captured the observed data better due to the impact of the change geometry. Moreover, the water quality results of S2 outperformed S1 regarding suspended solid, total nitrogen, and dissolved oxygen. This is attributed to the variables, such as flow travel time, which is directly related to the channel geometry. Additionally, SWAT was modified to consider the various channel cross-sectional shapes. The results of this study suggest that the channel geometry information for the water quantity and quality estimation should be carefully applied, which could improve the model performance regarding streamflow and water quality simulations.

Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

We mapped the extent of submerged aquatic vegetation (SAV) of Lake Iseo (Northern Italy, over the 2015–2017 period based on satellite data (Sentinel 2 A-B) and in-situ measurements; the objective was to investigate its spatiotemporal variability. We focused on the southern sector of the lake, the location of the shallowest littorals and the most developed macrophyte communities, mainly dominated by Vallisneria spiralis and Najas marina. The method made use of both in-situ measurements and satellite data (22 Sentinel 2 A-B images) that were atmospherically corrected with 6SV code and processed with the BOMBER (Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images). This modeling system was used to estimate the different substrate coverage (bare sediment, dense stands of macrophytes with high albedo, and sparse stand of macrophytes with low albedo). The presented results substantiate the existence of striking inter- and intra-annual variations in the spatial-cover patterns of SAV. Intense uprooting phenomena were also detected, mainly affecting V. spiralis, a species generally considered a highly plastic pioneer taxon. In this context, remote sensing emerges as a very reliable tool for mapping SAV with satisfactory accuracy by offering new perspectives for expanding our comprehension of lacustrine macrophyte dynamics and overcoming some limitations associated with traditional field surveys.

Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

Stochastic Method for Evaluating Removal, Fate and Associated Uncertainties of Micropollutants in a Stormwater Biofilter at an Annual Scale

A stochastic method for evaluating the in situ mass balance of micropollutants in a stormwater biofilter, accounting for inlet and outlet loads and the evolution of pollutant mass in the filter media (ΔMsoil) at an annual scale, is proposed. In the field context, this type of calculation presents a number of methodological challenges, associated with estimating water quality for unsampled rain events, reconstituting missing or invalidated flow data and accounting for significant uncertainties associated with these estimations and experimental measurements. The method is applied to a biofiltration swale treating road runoff for two trace metals, Cu and Zn and six organic micropollutants: pyrene (Pyr), phenanthrene (Phen), bisphenol-A (BPA), octylphenol (OP), nonylphenol (NP) and bis(2-ethylhexyl) phthalate (DEHP). Pollutant loads were reduced by 27–72%. While organic micropollutants are likely to be lost to degradation or volatilization processes in such systems, dissipation could not be demonstrated for any of the organic micropollutants studied due to emissions from construction materials (case of BPA, OP, NP and DEHP) or high uncertainties in ΔMsoil (case of Pyr and Phen). The necessary conditions for establishing an in situ mass balance demonstrating dissipation, which include acquisition of data associated with all terms over a period long enough that uncertainty propagation is limited and the absence of additional sources of pollutants in the field, are discussed.

Water Quality–Based Environmental Flow under Plausible Temperature and Pollution Scenarios

AbstractThis study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed...

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.

Noise Removal Method for Unmanned Aerial Vehicle Data to Estimate Water Quality of Miharu Dam Reservoir, Japan

Lake Sakurako is a reservoir of the Miharu Dam in Fukushima Prefecture, Japan. The water quality of the small lake becomes significantly worse during the summer owing to the occurrence of blue-green algae. Therefore, water quality management is a serious problem. Because the primary method of water quality analysis is direct collection from the target water area, the analysis range is limited, and the analysis of the entire water area is very difficult. Therefore, performing a wider range of analyses by remote sensing is a possible solution. In this study, we analyze near infrared (NIR) data acquired by unmanned aerial vehicles (UAVs). A fuzzy regression analysis is conducted on the UAV data and water measurements. Based on the experimental results of data from August 2015, the NIR data is confirmed to be useful in estimating the water quality conditions in Lake Sakurako. Furthermore, we investigate the noise removal process using a nonlocal mean filter and demonstrate that the process provides more detailed information regarding the lake’s water quality.

Hydrogeochemical processes in the groundwater environment of Batlagundu block, Dindigul district, Tamil Nadu: conventional graphical and multivariate statistical approach

Water quality assessment is essential to ensure viable safe use of the resources for drinking, agricultural and industrial purposes. Deterioration of groundwater quality due to anthropogenic activities is increasing at an alarming rate in most parts of Dindigul district, but limited work has been carried out on groundwater quality and monitoring. This paper highlights the groundwater quality and compares its suitability for drinking and irrigation purpose of Batlagundu block in Dindigul district of Tamil Nadu. Eighteen groundwater samples were collected systematically in triplicates during the year 2013–2014. The physical and chemical parameters of the analytical results of groundwater were compared with the standard guideline values recommended by the Bureau of Indian Standards and World Health Organization for irrigational suitability, domestic usage. Further hydrogeochemical data have been plotted in standard graphs such as Piper trilinear diagram, US Salinity Laboratory (USSL), Giggenbach triangle diagram and Schoeller diagram. The mathematical models are used to estimate water quality parameters and to describe realistic water situations. It is proved that electrical conductance is an important water quality parameter. Hydrofacies diagram reported that alkaline earths (Ca2+ and Mg2+) significantly exceed the alkalis (Na+ and K+), while acids of (Cl− and HCO3−) exceed the acids of (SO42− and CO32−). Therefore, the chemical compositions of the study area were characterized by the Ca–Mg–HCO3 water type and Na–Cl type. USSL diagram of the study area revealed that most of the sampling sites possess high salinity and low-to-medium sodium hazard indicated that water samples are poorly suitable for usual agropurposes. Giggenbach triangular diagram indicated that the samples have not gained equilibrium with their host rocks, presumably due to fact circulation of fluid through the rock features. The present research work thus concludes that groundwater in the study area is chemically unsuitable for domestic and agricultural uses. It is recommended to carry out a continuous water quality monitoring program and development of effective management practices for utilization of water resources.

RESEARCH ON AQUATIC POLLUTION LEVEL OF MALEIA RIVER BY SIMULATION IN COMPUTATIONAL FLUID DYNAMICS

Watercourses that transit rural / urban areas are susceptible to the phenomenon of aquatic pollution due to various types of polluting species. The important issues of river pollution and of polluting species dispersion, require an approach with predictive tools (models for transport of polluting species) which can evaluate the performance of depollution measures/actions to reduce pollution and to take optimal management decisions. Thus, from January 2017 to May 2017, a monitoring investigation was carried out on the Maleia watercourse, polluting species concentrations measured by different physical-chemical methods being considered as input data for the Surface Water Modelling Systems (SMS) software in order to establish the dispersion of pollutants in the aquatic environment. The hydrodynamics of the river sector has been simulated through a module of the Surface Water Modelling Systems using the Reynolds form of Navier-Stokes equation system, along with the continuity equation for incompressible fluids in turbulent motion with free surface. The numerical simulation of advection-diffusion processes at an average depth of the studied river sector, was used for analyzing the space-time evolution of aquatic pollutants. The originality of this paper starts from the desideratum that rivers crossing inhabited areas are subjected to discharge of pollutants which implies the analysis of the quality of the studied water course (Maleia), as well as the illustration of the dispersion of certain pollutant species by estimating the dilution times as well as the determination of the iso-concentration field. Numerical models have been obtained for a sector of the Maleia River, providing the possibility of simulating both common and accidental pollution (related to space-time evolution of transport and dispersion of pollutants). The models obtained allowed the estimation of water quality in each finite element of the studied sector, and not just in one sampling point, as it is usually measured.

Estimation of water quality, management and risk assessment in Khyber Pakhtunkhwa and Gilgit-Baltistan, Pakistan

Estimation of water quality, management and risk assessment in Khyber Pakhtunkhwa and Gilgit-Baltistan, Pakistan

Assessment of a Non-Optical Water Quality Property Using Space-Based Imagery in Egyptian Coastal Lake

Progressively anthropogenic intrusion and increasing water demand necessitate frequent water quality monitoring for sustainability management. Unlike laborious, time consuming field-based measurements, remote sensing-based water quality retrieval proved promising to overcome difficulties with temporal and spatial coverage. However, remotely estimated water quality parameters are mostly related to visibility characteristic and optically active property of water. This study presents results of an investigated approach to derive oxygen-related water quality parameter, namely Dissolved Oxygen (DO), in a shallow inland water body from satellite imagery. The approach deduces DO levels based on interrelated optical properties that dictate oxygen consumption and release in waters. Comparative analysis of multiple regression algorithms was carried out, using various combinations of parameters; namely, Turbidity, Total Suspended Solids (TSS), Chlorophyll-a, and Temperature. To cover the wide range of conditions that is experienced by Edku coastal lake, ground truth measurements covering the four seasons were used with corresponding satellite imageries. While results show successful statistically significant correlation in certain combinations considered, yet optimal results were concluded with Turbidity and natural logarithm of temperature. The algorithm model was developed with summer and fall data (R2 0.79), then validated with winter and spring data (R2 0.67). Retrieved DO concentrations highlighted the variability in pollution degree and zonation nature within that coastal lake, as related to boundary interactions and irregularity in flow dynamics within. The approach presented in this study encourages expanded applications with space-based earth observation products for exploring non-detectable water quality parameters that are interlinked with optically active properties in water.

Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis

Drinking water sources may be polluted by various pollutants depending on geological conditions and agricultural, industrial, and other human activities. Ensuring the safety of drinking water is, therefore, of a great importance. The purpose of this study was to assess the quality of drinking groundwater in Bardaskan villages and to determine the water quality index.Water samples were taken from 30 villages and eighteen parameters including calcium hardness (CaH), total hardness (TH), turbidity, pH, temperature, total dissolved solids (TDS), electrical conductivity (EC), alkalinity (ALK), magnesium (Mg2+), calcium (Ca2+), potassium (K+), sodium (Na+), sulphate (SO42−), bicarbonate (HCO3−), fluoride (F−), nitrate (NO3−), nitrite (NO2−) and chloride (Cl−) were analyzed for the purpose for this study. The water quality index of groundwater has been estimated by using the ANFIS. The spatial locations are shown using GPS. The results of this study showed that water hardness, electrical conductivity, sodium and sulfate in 66, 13, 45 and 12.5% of the studied villages were higher than the Iranian drinking water standards, respectively. Based on the Drinking Water Quality Index (DWQI), water quality in 3.3, 60, 23.3 and 13.3% of villages was excellent, good, poor and very poor, respectively.•Groundwater is one of the sources of drinking water in arid and semi-arid regions such as Bardaskan villages, which monitor the quality of these resources in planning for improving the quality of water resources.•The DWQI can clearly provide information associated with the status of water quality resources in Bardaskan villages.•The results of this study clearly indicated that with appropriate selection of input variables, ANFIS as a soft computing approach can estimate water quality indices properly and reliably.•Some parameters were in the undesirable level is some villages. Therefore, the government should try to improve the chemical and physical quality of drinking water in these areas with the necessary strategies.

PREDICTION OF WATER QUALITY INDEX OF AN INDIAN RIVER USING ARITHMETIC INDEX AND REGRESSION MODELS

This paper focuses on the effect of some water quality parameters of river water which helps in the calculation of water quality index (WQI) that culminates in the development of a regression model for prediction of WQI of the river system in India. The index was calculated by arithmetic index method using twelve various experimentally estimated water quality parameters such as potential hydrogen, chlorides, dissolved oxygen saturation, nitrates, sulphates, phosphates, total dissolved solids, biochemical oxygen demand, electrical conductivity, total hardness, turbidity and total coliform of the water at eight locations, for a 55 km stretch of Chalakkudy river November 2013 to December 2018. It was identified that total coliform is the major parameter contributing to the bad quality of water. Water quality regression model has been developed as a function of total coliform content. The performance of the model in predicting the WQI has been tested by comparing with the calculated WQI for the following year 2018. The regression model has been found to be good with an absolute average relative error and root mean square error values of 0.693 and 0.5 respectively. The results indicate that the basin is slowly getting into a serious drinking water crisis.

Assessment of a Non-Optical Water Quality Property Using Space-based Imagery in Egyptian Coastal Lake

Abstract Progressive anthropogenic intrusion and increasing water demand necessitate frequent water quality monitoring for sustainability management. Unlike laborious, time consuming field-based measurements, remote sensing-based water quality retrieval proved promising to overcome difficulties with temporal and spatial coverage. However, remotely estimated water quality parameters are mostly related to visibility characteristic and optically active property of water. This study presents results of an investigated approach to derive oxygen –related water quality parameter, namely Dissolved Oxygen (DO), in a shallow inland water body from satellite imagery. The approach deduces DO levels based on interrelated optical properties that dictate oxygen consumption and release in waters. Comparative analysis of multiple regression algorithms was carried out, using various combinations of parameters; namely, Turbidity, Total Suspended Solids (TSS), Chlorophyll-a, and Temperature. To cover the wide range of conditions that is experienced by Edku coastal lake, ground truth measurements covering the four seasons were used with corresponding satellite imageries. While results show successful statistically significant correlation in certain combinations considered, yet optimal results were concluded with Turbidity and natural logarithm of temperature. The algorithm model was developed with summer and fall data (R2 0.79), then validated with winter and spring data (R2 0.67). Retrieved DO concentrations highlighted the variability in pollution degree and zonation nature within that coastal lake, as related to boundary interactions and irregularity in flow dynamics within. The approach presented in this study encourages expanded applications with space-based earth observation products for exploring non-detectable water quality parameters that are interlinked with optically active properties in water.

Advanced In Situ Soil Water Sampling System for Monitoring Solute Fluxes in the Vadose Zone

Core Ideas The monitoring system synchronizes soil water extraction with real drainage periods. It is remote controllable and has SMS notification in case of automated sampling initiation. It is easy to duplicate using commonly available measuring equipment. The system can monitor field soil solute fluxes, such as contaminant migration. To estimate potential risks of groundwater contamination, national and international environmental legislation stipulates standard values referred to pollutant contents in the soil and more rarely referred to loads in the soil leachate. Although in situ soil leachate analysis yields more realistic drainage water quality estimates than soil contamination level–derived estimates, there is no existing standard for how to explicitly sample soil leachate for the required contaminant migration detection. The objective of this study was to overcome current limitations of soil seepage sampling for detecting a contaminant migration in the unsaturated zone by introducing a technical solution that automatically restricts soil water extraction to drainage periods using active devices such as suction cups. Sampling is triggered by a moisture threshold parameterized according to the respective soil water retention properties defining the onset of a drainage period. We tested our sampling approach on two different bioretention systems in Germany for stormwater drainage quality analysis out of the upper soil layer. We present the monitoring results of the 4‐mo testing phase containing 19 individual storm events illustrating the fundamental functioning of the in situ soil leachate sampling system under different climatic conditions. The results clearly demonstrate the feasibility of restricting soil water extraction to drainage periods by means of actual soil moisture measures and indicate a general transferability of our approach. Our approach is easily duplicable, based on the included technical description, for further studies requiring explicit soil leachate sampling and is likely to help improve the reliability of field‐monitored pollutant migration from contaminated sites.

Opportunities for natural infrastructure to improve urban water security in Latin America.

Governments, development banks, corporations, and nonprofits are increasingly considering the potential contribution of watershed conservation activities to secure clean water for cities and to reduce flood risk. These organizations, however, often lack decision-relevant, initial screening information across multiple cities to identify which specific city-watershed combinations present not only water-related risks but also potentially attractive opportunities for mitigation via natural infrastructure approaches. To address this need, this paper presents a novel methodology for a continental assessment of the potential for watershed conservation activities to improve surface drinking water quality and mitigate riverine and stormwater flood risks in 70 major cities across Latin America. We used publicly available geospatial data to analyze 887 associated watersheds. Water quality metrics assessed the potential for agricultural practices, afforestation, riparian buffers, and forest conservation to mitigate sediment and phosphorus loads. Flood reduction metrics analyzed the role of increasing infiltration, restoring riparian wetlands, and reducing connected impervious surface to mitigate riverine and stormwater floods for exposed urban populations. Cities were then categorized based on relative opportunity potential to reduce identified risks through watershed conservation activities. We find high opportunities for watershed activities to mitigate at least one of the risks in 42 cities, potentially benefiting 96 million people or around 60% of the urbanites living in the 70 largest cities in Latin America. We estimate water quality could be improved for 72 million people in 27 cities, riverine flood risk mitigated for 5 million people in 13 cities, and stormwater flooding mitigated for 44 million people in 14 cities. We identified five cities with the potential to simultaneously enhance water quality and mitigate flood risks, and in contrast, six cities where conservation efforts are unlikely to meaningfully mitigate either risk. Institutions investing in natural infrastructure to improve water security in Latin America can maximize their impact by focusing on specific watershed conservation activities either for cleaner drinking water or flood mitigation in cities identified in our analysis where these interventions are most likely to reduce risk.

Estimation of pollution level of lake Yalpuh as an object of fishing purpose

The work offers estimation of Lake Yalpuh's water quality for a long period as a fishery object. The preliminary analysis of the dynamics of water quality indicators change showed that there was a decrease of concentrations of almost all quality indicators during the period of the study (2003 - 2017).
 According to the results of the estimation, the water quality of Lake Yalpuh in the top section was characterized by the "very dirty" category, the quality class was marked as IVc-IVd. Most often such indicators as dry residue, magnesium, chlorides, sulfates were taken as limiting indicators of pollution. During the research the quality of lake water was did not change significantly. The maximum level of pollution was observed in 2004.
 The water of the lake at the water intake section was also characterized by the "very dirty" category, the quality class was marked as IVb - IVd. Most often such indicators as magnesium and sulfates were taken as limiting indicators of pollution. During the period of research there was a tendency towards a decrease of the lake's level of water pollution. The maximum level was observed in 2003 – 2004.
 The waters of the lake in the 13th km section was characterized by the "dirty" (quality class ІІІa-ІІІb) and "very dirty" (quality class IVa-IVc) categories. Most often such indicators as magnesium and sulfates were taken as indicators of limiting pollution. Significant fluctuations of the level of the lake water pollution were observed. The maximum level was observed in 2004.
 The general analysis of the dynamics of change of the combinatorial index of water pollution in Yalpuh Lake at three sections for 2003 - 2017 showed that the maximum level of water pollution in the lake was observed at the first section (top), the minimum – at the 13th km section. There is a tendency towards decrease of the value of the combinatorial index of pollution.
 Virtually the entire territory of the Yalpuh River Basin is located at the southern part of Moldova where the state of wastewater treatment is unsatisfactory. Therefore, the level of pollution in the lake often exceeds the permissible standards. In recent years, due to decrease mineral fertilizers and pesticides use in Moldova's agricultural sector, the situation improved though.