Estimate Water Quality Research Articles

Application of M5 model tree optimized with Excel Solver Platform for water quality parameter estimation.

The high cost and time for determining water quality parameters justify the importance of application of mathematical models in discovering connection among them. This paper presents a data mining technique and its improved version in estimating water quality parameters. For this purpose, the surface and ground water quality data from Hamedan (Iran) between 2006 and 2015 were analyzed using M5 model tree and its modified version optimized with Excel Solver Platform (ESP). The values of electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), and total hardness (TH) were considered as target variables, whereas pH, concentrations of sodium (Na), chlorine (Cl), bicarbonate (HCO3), sulfate (SO4), magnesium (Mg), calcium (Ca), and potassium (K) were as inputs. The results showed that in both the sources, pH was the least influential parameter on EC, TDS, SAR, and TH. It was found that among the objective parameters, the accuracy of models in estimating TH was higher than the other parameters, whereas SAR was a complex variable. The comparison of performances of the M5 and the M5-ESP models illustrated that the application of the ESP significantly decreased the normal root mean error (NRMSE) of the M5 model; the mean NRMSEs were decreased by 18.95% and 20.29% in estimating groundwater and surface water quality parameters, respectively. Moreover, ability of both the M5 and the M5-ESP models in computing objective parameters of the groundwater was found to be better than the surface water.

Kinetics of Quality Changes in Soaking Water during the Retting Process of Pepper Berries (Piper nigrum L.)

There are organic matters and bioactive compounds naturally present in pepper that may have leached out into the soaking water due to a prolonged retting process that caused changes in water quality. This study was carried out to determine the influences of different quantities of mature pepper berries and soaking time on the quality of soaking water during the retting process. The soaking test was conducted by having three tanks with different quantities of pepper berries soaked in the 18 L of water. The results show that the tank with the highest quantities of pepper berries has the highest increment of turbidity from 21.80 ± 1.90 NTU to 1103.30 ± 23.10 NTU (98%), the highest reduction in pH from 6.99 ± 0.02 to 3.67 ± 0.02 (47.50%), the highest reduction in dissolved oxygen from 5.19 ± 0.17 mg/L to 1.05 ± 0.02 mg/L (79.77%) and the highest increment of chemical oxygen demand from 21.67 ± 1.15 mg/L to 3243.33 ± 5.77 mg/L (99.33%) compared to other tanks. Furthermore, the zero, first and second-order kinetic models fitted well with the experimental data of the quality of soaking water for three conditions using the Arrhenius law approach. Thus, these findings are useful for estimating water quality during the retting process.

COVID-19 Pandemic Consequences on Coastal Water Quality Using WST Sentinel-3 Data: Case of Tangier, Morocco

The west coast of Tangier, in northern Morocco, has been affected by industrial wastewater discharge that reaches the ocean through the Boukhalef river. Therefore, the Jbila and Sidikacem beaches near to the Boukhalef river mouth have been classified as polluted for many years. With the aim of determining the COVID-19 pandemic consequences on the Tangier coastal environment, a linear model using Sentinel 3 water surface temperature (WST) has been tested in several locations. Data from April 2019 and April 2020, before and during the COVID-19 pandemic related emergency status in Morocco, were compared. The results from April 2019 showed high WST values and consequently, the poorest water quality in the sites closest to the Boukhalef river mouth. On the other hand, the results from April 2020 showed normal WST values and high water quality in the same study area. These results illustrate the usefulness of Sentinel 3 WST for the estimation of bathing water quality on the west coast of Tangier. The study shows the positive impact of the COVID-19 pandemic consequences on the coastal environment quality in the study area and indicates the importance of decreasing the industrial discharge on the west coast of Tangier. The same methodology could be used in decision-making processes and to reduce cost, time and human resources for coastal monitoring systems. We demonstrate the potential of using the Sentinel 3 data for coastal waters monitoring, as well as the need for stricter controls of pollutant discharges into the world’s rivers.

Exploring the effectiveness of drainage water management on water budgets and nitrate loss using three evaluation approaches

Subsurface (tile) drainage is a necessary practice for economic crop production in humid, poorly drained regions of the world. Drainage water management (DWM), also known as controlled drainage, is among the suite of practices designed to mitigate water quality issues associated with subsurface drainage. Studies evaluating DWM generally include field experiments or simulation models to estimate water quality impacts. Combining field studies with model simulations can provide a more comprehensive assessment. Furthermore, using multiple approaches can provide a range of effectiveness rather than a single estimate, which can help define the uncertainty associated with DWM effectiveness. The goal of this study was to demonstrate the variability in estimated DWM effectiveness using 10 years of measured data and a calibrated hydrologic model. The study field, located in central Ohio, was under a free drainage (FD) mode from 2005 through 2008, and under DWM from 2009 through 2014. We used three approaches to compare discharge and nitrate concentrations and loads: before-and-after, paired-field, and DRAINMOD-NII simulated scenarios. Based on the three evaluation approaches, DWM increased surface runoff by 50% (∼13 mm), reduced annual drainage discharge between 8 and 23% (∼19 to 69 mm), annual mean NO3-N concentrations between −1 and 2.5%, and NO3-N loads between 9 and 25% (∼2.8 to 10.3 kg ha−1 yr−1). DWM also reduced the frequency of drainage discharge, and increased magnitudes of surface runoff, evapotranspiration, and seepage losses. The true magnitude of DWM effectiveness remains somewhat uncertain, especially in fields prone to seepage and runoff losses. However, using a combined assessment approach can help address the uncertainty associated with estimated DWM effectiveness at watershed and regional scales.

Portable Photoelectrochromic Visualization Sensor for Detection of Chemical Oxygen Demand.

Chemical oxygen demand (COD) is an important parameter to estimate water quality, and many methods have been developed to measure COD. However, these methods are not satisfying because of the use of hazardous materials, the interference of chloride, and complicated operations. Meanwhile, the development of portable sensors for field testing COD is still at an early stage. In this work, we propose a new portable COD sensor based on the photoelectrochromic principle. The whole detection can be conducted with a small indium tin oxide (ITO) electrode modified with photocatalytic material TiO2/g-C3N4 and the electrochromic material Prussian blue. Under light illumination, organic pollution can be photocatalytically oxidized by TiO2/g-C3N4 and photogenerated electrons are transferred to Prussian blue, which will be reduced to colorless Prussian white. So the COD value can be obtained through the color change of the ITO electrode. By adjusting the electrode size, the detection range is flexible from 0.025 to 750 mg/L, and this sensor has a good repeatability and stability. Meanwhile, this portable visualization PEC sensor has been successfully applied in actual wastewater sample detection, which has a good prospect in COD monitoring.

Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

A Machine Learning Approach towards Automatic Water Quality Monitoring

Increasing rate of water pollution and consequently waterborne diseases are the engrossing evidence towards danger to living organisms. It becomes a great challenge these days to preserve our flora and fauna by controlling various unexpected pollution activities. Although the invention of many schemes and programmes regarding water purification has done a tremendous job, but still there is something that has been lagging. With increase in population, industrialization and global warming situation is getting worse day by day. It becomes very difficult to get safe drinking water and appropriate quality water for other domestic usage and agriculture purpose. Major reasons for water pollution include undesirable increase in impurities. These may cause eutrophication of the water body, change in taste, discolouration and odour of water, water borne diseases, increase in water toxic nature etc. For water to be serviceable it should be aesthetically acceptable, chemically safe, bacteria free; organic substances and radioactive elements should be absent. So, there is an urgent need to look into this situation and take the corrective and necessary actions to overcome this situation. The government is paying an attention to this problem and finding the ways to control the situation. However, major areas are not developed to the point and water quality estimation is totally dependent upon sampling at location and testing in laboratories. Manual sampling and measurements are prone to human errors and these techniques may create ambiguities in predicted output. In this paper we have presented Machine Learning (ML) approach for calculating the Water Quality Index (WQI) and classification of water quality to estimate water characteristics for usage. For analysis, decision tree method is used to estimate water quality information. The standard values of parameters are selected as per guidelines provided by World Health organization (WHO). Results calculated using ML techniques showed prominent accuracy over traditional methods. Accuracy achieved is also significant, i.e. 98 %. Likewise, projection of gathered data was done utilizing web interface and web app to alert the authorities about contamination.

Dissolution Test Protocol for Estimating Water Quality Changes in Minerals Processing Plants Operating With Closed Water Circulation

To save freshwater resources and comply with environmental regulations, minerals processing operations are transitioning to partially or fully closed water circulation. However, the accumulation of electrolytes and the addition of reagents lead to changes in water composition and may compromise flotation performance and plant maintenance. As a consequence, costly modifications are often required to cope with these challenges. Therefore, knowledge about water quality variation owing to closed water circulation and its potential effect on the flotation performance is crucial. The experimental methodology presented in this paper targeted three main objectives: (1) predicting the tendency of the accumulation of elements and compounds into the process water during comminution, flotation, and storage in tailings facilities; (2) establishing a relationship between laboratory results and plant historical water quality data; and (3) predicting the potential effect of recycling water on flotation performance. The results obtained with Boliden Kevitsa ore showed a good correlation between the water matrix of the actual process water on-site and that obtained in the ore dissolution tests done in the laboratory. The final water composition came close to the process water in terms of major elements and some of the minor elements. Additionally, the work presented in this paper demonstrated that a dissolution loop allowed us to predict the potential impact of the recycling water on the ore flotability. This methodology could serve as an aid for predicting water quality matrix variation and designing closed water circulation systems at existing and new plants.

Water quality index using fuzzy logic Utcubamba River, Peru

Water is a fundamental nutrient in the life of any living being. Therefore, it is necessary to estimate its quality, because it is an issue of increasing concern countries around the world for reasons such as the health of the population, regional, national and international economic development, and the environmental quality of the ecosystems. One tool that has been used to know the state of the water is the water quality indexes (WQI). The objective of this research was to develop a WQI based on fuzzy logic, which allows for the estimation of water quality in the Utcubamba River. The methodology used was proposed by Icaga in 2007. To evaluate the proposed WQI called "Diffuse Water Quality Index" (DWQI), sixteen points from the sampling conducted by the Research Institute for Sustainable Development during October 2014 on the Utcubamba River and its tributaries were used. To validate the index, it was necessary to estimate the correlation coefficient R2 between the results obtained and those of the NSF WQI wáter quality index reported by the Water Research Center. This new index presented results and reasonable correlation, R2 = 0.81. It is concluded that DWQI can be used as a tool for decision making in the water management of the Utcubamba River.

Estimation of surface water quality parameters based on hyper-spectral and 3D-EEM fluorescence technologies in the Ebinur Lake Watershed, China

Water quality research relies on field sampling, which is often very difficult to obtain, especially in arid areas. This study chose the Ebinur Lake Watershed in arid region as a study area. It analyzed 12 water quality parameters (WQPs) and hyper-spectral derived from 48 field samples. Parallel factor analysis (PARAFAC) method was employed to extract four fluorescent components from the fluorescence excitation-emission matrix (EEM) data. Four fluorescence spectral indices (Fn (355), the fluorescence index (FI), the humification index (HIX)) were used to characterize the organic matter. Estimated WQPs were then coupled with hyper-spectral and three-dimensional fluorescence technologies using the Back Propagation-Artificial Neural Network (BPANN) method developed in this study. The main findings are: (1) Higher correlations exist among the reflectance peaks, spectral indices (DI, NDI, RI) and WQPs, which is helpful to improve the accuracy of water quality estimation. (2) There are also high correlations among fluorescent components (peak) (C1, W2, W3, W4, W5 and W7), fluorescence spectral index and some of WQPs. This indicated that fluorescent components and fluorescence indices can be used to accurately monitor WQPs in surface water. (3) The BPANN model has a great potential for estimating WQPs, because of residual predictive deviation (RPD) of estimation model and verify model more than 1.4. These preliminary results have proved that hyper-spectral and fluorescence technologies is a valuable tool for monitoring surface water quality.

A simple and effective method for removing residual reflected skylight in above-water remote sensing reflectance measurements

In situ remote sensing reflectance (Rrs) measured using the above-water approach usually suffers from residual reflected skylight (Δ), and thus its applicability is limited. In this study, we proposed a simple method, which only required the in situ Rrs spectrum itself as input, to further remove the Δ effects in the in situ Rrs spectrum. The performance of the proposed method was evaluated using both simulation data and in situ Rrs spectra measured using a radiance sensor with a black tube (Δ-free in situ Rrs). The results showed that the proposed method outperformed other existing methods and can be applied to various types of waters. For example, from the two simulation experiments, it was seen that the proposed method can provide accurate Rrs spectra with a mean absolute percentage error (MAPE) = 6% even if they are measured under various sky conditions and wind speeds (using Hydrolight) and can estimate accurate Δ values with MAPE = 5% (using a simple bio-optical model). From a comparison analysis using Δ-free in situ Rrs data, the proposed method also showed the best performance with the smallest MAPE of 45% (MAPE ranged from 52% to 260% for other existing methods). In addition, the proposed method can improve the quality of Rrs spectra collected under various sky conditions (clear, scattered clouds and overcast), with an increased proportion of higher quality assurance scores. Two showcases indicate that the Δ corrections should be carried out for all above water approach-measured Rrs spectra before they are applied to develop water quality estimation algorithms or to evaluate the performances of atmospheric correction algorithms.

Water quality index analysis for portable and bottled waters

Abstract This research work aimed to investigate the degree of quality of tap and bottled waters available in Al-Madinah city through an experimental program comprising chemical, physical and biological tests. The study included examining the quality of the water supply network components such as taps, underground storage tanks and feeding pipes of the network. Filtration influence on the tap water was investigated by testing different water samples before and after the filtration process at different locations. The experiments also examined various bottled water brands sold in Al-Madinah. In addition, the impact of using the dispenser on the water quality of large bottled waters was studied. The water quality of two types of waters was compared by estimating water quality index (WQI). Results of this study revealed that the overall quality of the tap water is within standard values; however, deterioration occurs for the water through its journey in the water supply system. Results revealed that hardness and fluoride concentrations in bottled waters exhibited high values close to or more than allowable values of the standards. Furthermore, results confirmed that using the filter and dispenser decreased the water quality degree in the long run. Bottled waters showed a higher grade of WQI than tap water.

An investigation into the relationship between light absorption budget and trophic status in inland waters

The optical properties of the water can give comprehensive information about the in-water constituents which is directly related to concentration, size and composition, all essential for the development of bio-optical algorithms. Aiming to provide new observations about optical properties of water in two distinct reservoirs situated along the cascade system of Tietê River: Barra Bonita (BB) and Nova Avanhandava (Nav), this study described and discussed their inherent optical properties (IOPs) in terms of the absorption contribution. BB showed a dominance in the phytoplankton fraction at all visible wavelengths, and Nav by nonalgal particle absorption (aNAP), mainly at the blue and green wavelengths. The mass-specific absorption coefficient of phytoplankton at 443 nm observed in Nav and BB corroborated with the assumption that the trophic state decreases from oligotrophic to hypereutrophic. The aNAP(λ) did not show a seasonal pattern between field campaigns and reservoirs. The absorption coefficient of particulate enhanced the strong influence of phytoplankton pigments along the visible spectrum in BB, whereas Nav only displayed the peak at 675 nm represented by chlorophyll a absorption and high contribution of non-algal particle and colored dissolved organic matter in the blue region. These findings recommend the use of different modelling approaches to estimate water quality parameters from both sites when the inputs of the models consider the specific information about the IOPs and their relation with the in-water constituents.

Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing

ABSTRACT Monitoring of inland water quality is of significant importance due to the increase in water quality related issues, especially within the Midwestern United States. Traditional monitoring techniques, although highly accurate, are vastly insufficient in terms of spatial and temporal coverage. Using a virtual constellation by harmonizing Landsat-8 and Sentinel-2 data a high temporal frequency dataset can be created at a relatively fine spatial scale. In this study, we apply a novel deep learning method for the estimation of blue-green algae (BGA), chlorophyll-α (Chl), fluorescent dissolved organic matter (fDOM), dissolved oxygen (DO), specific conductance (SC), and turbidity. The developed model is evaluated against previously studied machine learning methods and found to outperform multiple linear regression (MLR), support vector machine regression (SVR), and extreme learning machine regression (ELR) generating R2 of 0.91 for BGA, 0.88, 0.89, 0.93, 0.87, and 0.84 for Chl, DO, SC, and turbidity respectfully. This model is then applied to all available data ranging from 2013–2018 and time series for each variable were generated for four selected waterbodies. We then use the Empirical Data Analytics (EDA) anomaly detection method on the time series to identify abnormal data points. Upon further analysis, the EDA method successfully identifies abnormal events in water quality. Our results also demonstrate strong correlation between non-optically active variables such as SC with Chl and fDOM. The framework developed in this study represents an efficient and accurate empirical method for inland water quality monitoring at the regional scale.

Pollution of Volga Mouth Area

Estimates of pollution of Volga mouth area in 2016 are given. Data on the distribution of water flow into the Lower Volga and the results of state environmental monitoring were used to calculate pollutant concentrations in water and bottom sediments in Volga mouth area. The results were obtained separately for the Volga delta, as well as the shallow and deep-water zones of the nearshore. Integrated estimates of water pollution level were obtained with the use of algorithms for calculation of complexity factor, combinatorial pollution index, and specific combinatorial pollution index. The integrated estimates of water quality in 2016 were dirty in the Volga delta, moderately polluted in the shallow nearshore zone, and pure in the deep-water nearshore zone.

Pond turtle carapaces, an alternative natural substrate for the use of a diatom-based water quality index

ABSTRACT Benthic diatoms are widely used as bio-indicators in water quality monitoring of rivers and streams, but less commonly in lentic waterbodies such as pools and lakes. Currently, a new method for assessing the ecological status of lakes is being tested in France to meet the requirements of the European Water Framework Directive (WFD) 2000/60/EC. This new method uses epilithic and epiphytic diatoms growing on stones and aquatic plants. Alternatively, freshwater turtles and the epizoic algae found on their carapaces may provide an excellent substrate for estimating water quality. Our study proposes an original diatom-based method for assessing the quality of water bodies in which turtles live. The main objective was to compare the Biological Diatom Index (BDI) values measured from epizoic diatom samples taken on the carapace of turtles with classical epilithic or epiphytic samples. Epizoic samples were taken from the carapaces of the turtle Emys orbicularis in two shallow French Mediterranean wetlands. Our results indicate that diatom assemblages differ slightly within the three substrates studied with limited variations in index value. However, some variability among epizoic samples from the same site suggests that several turtle individuals should be sampled for water quality biomonitoring.

Coupled approach for radiometric calibration and parameter retrieval to improve SPM estimations in turbid inland/coastal waters.

High-precision radiometric calibration (RC) coefficients are required to retrieve reliable water quality parameter products in turbid inland/coastal waters. However, unreliable RC coefficients when satellite sensors lack accurate and in-time RC may lead to pronounced uncertainties in the products through error propagation. To address this issue, a novel approach for estimating water quality parameters, taking suspended particulate matter (SPM) as a case, was proposed by coupling the procedures of RC and SPM model development. The coupled models were established using digital numbers (DNs) from target sensors and "in-situ" SPM measurements from concurrent well-calibrated reference sensors, with the RC coefficients introduced as unknown model parameters. The approach was tested and validated in varied Chinese inland/coastal regions, including the Hongze lake (HL), Taihu lake (TL), and Hangzhou bay (HB). The results show: (1) the DN-based SPM models can achieve a degree of accuracy comparable to reflectance-based SPM models with determination coefficients (R2) of 0.94, 0.92, and 0.72, and root-mean-square errors (RMSE) of 7.02 mg/L, 15.73 mg/L, and 619.2 mg/L for the HL, TL, and HB, respectively, and the biases less than 3% between the derived and official gain RC coefficients; (2) the uncertainty of SPM products increases exponentially as the RC uncertainty increases for exponential reflectance-based SPM models; (3) the DN-based SPM models are less sensitive to the uncertainties of atmospheric correction and RC coefficients, while the reflectance-based models suffer deeply. This study provides encouraging results to the improvement of SPM retrieval using the DN-based models by coupling RC and SPM retrieving processes, especially for sensors without precise RC coefficients.

Can bioenergy carbon capture and storage aggravate global water crisis?

Bioenergy carbon capture and storage (BECCS) is an effective option for mitigation of greenhouse gas (GHG) emissions. Nevertheless, there is barely serious debate about whether its implementation can possibly jeopardize the global water resources security. Here, we provided an assessment of biomass-based Substitute Natural Gas (BioSNG) production combined with CCS, a promising BECCS technology, in terms of global water resources security, with a focus on the growth of two typical second-generation bioenergy crops: switchgrass and miscanthus. A bottom-up analysis approach was applied in this paper to calculating water consumption for BECCS and estimating water quality deterioration caused by increasing fertilizer and pesticide application. The results indicated that water usage of BECCS was equal to adding 12.86%–16.64% (switchgrass) and 17.59%–26.06% (miscanthus) additional water stress on global available water resources at 2100. Additional N fertilizer application in 2100 would be equal to over 84%, 55% and 42% for both switchgrass and miscanthus under three CCS capture efficiency scenarios, respectively, comparing to such global scale in 2012. Additional phosphate fertilizer adding to global annual available water at 2100 were 0.004–0.008 mg L−1 (switchgrass) and 0.003–0.006 mg L−1 (miscanthus), respectively. The secondary environmental hazards, such as N2O emission, would offset GHG emission mitigation by BECCS. Meanwhile, the enrichment and leaching of pesticide residues increased the risk of groundwater contamination. This study revealed water consumption and contamination issues caused by BECCS cannot be neglected. Thus, additional studies of accurate land-use models in global scale and advanced technology for biofuel extraction are needed in the future.

Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

Influence of sanitary – domestic conditions in the rural settlements of dnepropetrovsk region on the peasants’ healh

The aim: Implementation results of sociological survey to the practice of hygienic researches at the subjective estimation of the drinking water quality and sanitary conditions of people's life. Materials and methods: At our study was used standardized "Questionnaire survey about quality of piped drinking water, which enters to the building (apartment)", which contained 25 questions. In the sociological survey were participated 150 respondents. Results: Peasants of Dnepropetrovsk region considered that the following indicators of drinking water quality impact on their health: deterioration quality of piped drinking water, smell - 22.6%, color - 14.6 %, rust - 13.3 %, precipitate - 8.0 %, turbidity and suspended solids - 10.6 %, taste and aftertaste - 13.3 %, foam and impurities - 10.6% (p<0.05). Some of peasants (14.6 %) have unsatisfactory sanitary conditions, living on 5 - 6 persons in 1 rooms flat (p<0.05). Conclusions: Results of sociological survey shown that among rural residents increased the demand for a consumption of pre-treated drinking water, due to the deterioration quality of tap water in the rural area. It was determined, that increased frequency of use the bottled drinking water - daily it was used by 13 rural respondents (17.3%), once a week - 21 respondents (28%), 2-3 times a week - 8 respondents (10.6%).