scholarly journals Assessment of water quality if the Argazin reservoir and peculiarities of the bottom sediments impact upon it.

2021 ◽  
pp. 6-23
Author(s):  
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Chemical Composition ◽  
Bottom Sediments ◽  
Water Area ◽  
Hydrodynamic Modeling ◽  
Migration Routes ◽  
Chelyabinsk Oblast ◽  
Chemical Indicators ◽  
Technological Impact

The article presents the results of research on the chemical composition of the water of the largest reservoir in the Chelyabinsk Oblast that is the Argazin reservoir, which historically experiences technological impact of the metallurgical facilities. The connectivity of the main chemical indicators of water quality was evaluated using cluster analysis. Hydrodynamic modeling was carried out, which enabled to determine the migration routes of pollutants (AR) through the water area of the Argazin reservoir, to predict the concentrations of AR in the event of their input through the Miass River. Conclusions have been made about the significant self-purification ability of the reservoir. The main unfavorable factors in which water quality in the reservoir may deteriorate were identified.

scholarly journals Many-year Dynamics and Current Trends of the Pavlovsk Reservoir Area Water Chemical Composition Changes

2020 ◽  
pp. 19-34
Author(s):  
Keyword(s):  
Chemical Composition ◽  
Water Concentration ◽  
Petroleum Products ◽  
Water Area ◽  
Modern Trend ◽  
Reservoir Water ◽  
Chemical Indicators ◽  
Chemical Index ◽  
Chemical Composition Of Water ◽  
Chemical Studies

At present, changes have occurred in the basin and water area of the Pavlovsky reservoir, affecting the chemical composition of the reservoir water. The combination of many factors (series of low-water years, termination of milk alloy, increase of recreational load, change of the share of diffuse contamination, etc.) do not allow to predict the modern trend of transformation of chemical composition of water. Considering that no gydro/chemical studies of Pavlovsky reservoir have been carried out in recent decades, studies of modern processes of transformation of chemical composition of this reservoir are relevant. For this purpose BashNIIVKh in 2018-2019 carried out hydro/chemical studies of the reservoir, in two years water samples were taken from the surface and from the bottom, as well as bottom deposits. In 2018 samples were taken according to 30 chemical indicators on 10 struts, and in 2019 – according to 10 chemical indicators on 7 struts. Also, the results of previous studies have been analyzed. Due to the large volume of the obtained material, this work only considered the results of studies of multi-year dynamics and modern trend of change of chemical composition of water (concentration of chemicals) in the water area of the Pavlovsky reservoir for 10 chemical indicators of water on 7 branches. Concentrations of Sr, Mn, petroleum products are shown to decrease along the length of the reservoir in the water; Increasing - Zn, Hg, phenols of organic substances; evenly distributed – Fetotal, Ptotal, Cu. In the many-year dynamics, Fetotal, Mn, petroleum products, phenols (in the inlet), COD show a trend of decrease of chemical indicators in water; Upward trend – Ptotal, Cu, Zn, phenols (in the exit). It has been established that the average annual concentrations of chemical indicators in the water of the Pavlovsky reservoir are related to the average annual inflow. The chemical values of Fe and Mn have a direct link (when the inflow increases, the concentration of the chemical index increases), and Ptotal, Cu, Zn, phenols and COD have a feedback (when the inflow increases, the concentration of the chemical index decreases).

Dynamics of change monitoring of sanitary and chemical indicators of water quality in springs in the subarid region by the model of Saratov region

2020 ◽  
pp. 54-64
Author(s):  
Ksenia Aleksandrovna Savina ◽  
Dmitry Evgenievich Ivanov ◽  
Anatoly Nikolayevich Mikerov ◽  
Vladimir Fedorovich Spirin ◽  
Anton Valerievich Kosarev ◽  
...  
Keyword(s):  
Water Quality ◽  
Chemical Composition ◽  
Spring Water ◽  
Total Hardness ◽  
Saratov Region ◽  
Chemical Indicators ◽  
Alternative Sources ◽  
The Right ◽  
The City

Springs on the right Bank of the Saratov region can be considered as a predictive model of changes in the chemical composition of spring water in a warming climate. The 14 sources most demanded by the population were identified. The probable causes of the detected deviations in the investigated sanitaryhygienic indicators are also described. The research period is 2016–2019. The sources are located on the territory of the right Bank of the Saratov region, including the city of Saratov. We determined the flow rate and type of sources, the water temperature in them, the presence of captage and sanitary protection zones, the chemical composition of water in terms of smell, taste, color, turbidity, pH, total hardness, total mineralization, Cl, SO4 2-, NO3 , NO2 , NH3 , Pb2+, Cd2+, Fecom, Mncom, Crcom, Ascom. We found that 35 % of samples do not meet the requirements of sanitary standards. Deviations were detected in terms of color, turbidity, total hardness, manganese, chlorides, nitrates, and cadmium. The dynamics of changes in chemical indicators of water quality in all surveyed sources was studied. In 6 of them, from 2016 to 2019, an increase in the level of hardness and total mineralization was revealed. The highest concentrations of the studied substances were recorded in the spring period (flood). An increase in the concentration of manganese was observed in 6 sources. In one of them, in March 2017, the critical value of this indicator was recorded (22 MPC). The necessity of using springs by the population as alternative sources of water supply for drinking purposes is analyzed. To identify the demand for springs in the city of Saratov, a survey of 535 people was conducted directly at the sampling sites. The conducted survey of the population indicates the confidence of the population in the quality of spring water. The probable causes of the detected excess are described. A comparison of our results with available data from 2009–2016 indicates a deterioration in the water quality of the surveyed sources.

Comprehensive monitoring of the Klyazma reservoir

2020 ◽  
pp. 17-25
Author(s):  
Vikenty L. Razumovsky
Keyword(s):  
Chemical Composition ◽  
Bottom Sediments ◽  
Environmental Changes ◽  
Water Area ◽  
Graphical Analysis ◽  
Anthropogenic Factors ◽  
Layer By Layer ◽  
Reservoir Water ◽  
Chemical Composition Of Water ◽  
Diatom Complexes

The research was carried out during the comprehensive monitoring of the Klyazma reservoir in 2019. The work is devoted to the assessment of long-term environmental changes under the influence of natural and anthropogenic factors. The research combines two traditional methods of analysis. A layer-by-layer study of diatomic complexes and chemical composition in the bottom sediments of the reservoir was carried out. The results obtained were compared with the results of comprehensive monitoring on the taxonomic composition of phytoplankton and the chemical composition of water in the reservoir's water area. The results of previous studies on the chemical composition of water in the reservoir were used. In addition to the traditional forms of chemical and hydrobiological analysis, an innovative method of graphical analysis was applied to phytoplankton complexes and the principle of unification of bioindication methods was applied for diatom complexes from sediment columns. The main scenarios of taxonomic proportions transformations in phytoplankton and diatom complexes were determined using the method of graphical analysis for the Klyazma reservoir water area. The transformations are caused by the influence of water from the Moscow channel and the planned annual lowering of the water level in the reservoir. Bottom sediments showed a regular increase in the reservoirs level of saprobisation. Presumably, this is due to a non-linear increase in the number of tourists and an cahnges in recreational load. Pronounced accumulation of Cu, Zn and other heavy metals in the coastal zone of the reservoir was confirmed.

Analysis of long-term water quality changes in the Kis-Balaton Water Protection System with time series-, cluster analysis and Wilks’ lambda distribution

2011 ◽  
Vol 37 (4) ◽  
pp. 629-635 ◽  
Author(s):  
István Gábor Hatvani ◽  
József Kovács ◽  
Ilona Székely Kovács ◽  
Pál Jakusch ◽  
János Korponai
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Cluster Analysis ◽  
Protection System ◽  
Water Protection ◽  
Quality Changes ◽  
Wilks Lambda

scholarly journals Long-Term River Water Quality Trends and Pollution Source Apportionment in Taiwan

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1394 ◽  
Author(s):  
Marsha Putri ◽  
Chao-Hsun Lou ◽  
Mat Syai’in ◽  
Shang-Hsin Ou ◽  
Yu-Chun Wang
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Heavy Metal ◽  
Cluster Analysis ◽  
River Water ◽  
Principal Component ◽  
Pollution Level ◽  
Metal Levels ◽  
Metal Monitoring

The application of multivariate statistical techniques including cluster analysis and principal component analysis-multiple linear regression (PCA-MLR) was successfully used to classify the river pollution level in Taiwan and identify possible pollution sources. Water quality and heavy metal monitoring data from the Taiwan Environmental Protection Administration (EPA) was evaluated for 14 major rivers in four regions of Taiwan with the Erren River classified as the most polluted river in the country. Biochemical oxygen demand (6.1 ± 2.38), ammonia (3.48 ± 3.23), and total phosphate (0.65 ± 0.38) mg/L concentration in this river was the highest of the 14 rivers evaluated. In addition, heavy metal levels in the following rivers exceeded the Taiwan EPA standard limit (lead: 0.01, copper: 0.03, and manganese: 0.03) mg/L concentration: lead-in the Dongshan (0.02 ± 0.09), Jhuoshuei (0.03 ± 0.03), and Xinhuwei Rivers (0.02 ± 0.02) mg/L; copper: in the Dahan (0.036 ± 0.097), Laojie (0.06 ± 1.77), and Erren Rivers are (0.05 ± 0.158) mg/L; manganese: in all rivers. A total 72% of the water pollution in the Erren River was estimated to originate from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research demonstrated that applying PCA-MLR and cluster analysis on long-term monitoring water quality would provide integrated information for river water pollution management and future policy making.

scholarly journals Influence of Bottom Sediments Physicochemical Characteristics on Distribution of Microelements in the Crimean Shelf Water Areas

2020 ◽  
Author(s):  
E. A. Kotelyanets ◽  
K. I. Gurov ◽  
◽  
Keyword(s):  
Heavy Metals ◽  
Trace Elements ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Bottom Sediments ◽  
Clay Fraction ◽  
Zinc Content ◽  
Physicochemical Characteristics ◽  
Water Area ◽  
Pollution Level

The paper presents the results of studies of the Kalamitsky Bay bottom sediments pollution level with microelements and heavy metals (Pb, Zn, Cu, Ni, Co, Cr, V, As, Sr, Ti, Fe, Mn) in comparison with the content of these microelements and heavy metals in the Balaklava Bay sediments. The paper analyzes data obtained during expeditions on the R/V Professor Vodyanitsky in the Kalamitsky Gulf in August 2011 (seaward part) and on the R/V Rioni in September 2012 (coastal area) as well as in the Balaklava Bay in October 2018. Content of microelements and heavy metals in bottom sediments of the studied water areas was determined by the X-ray fluorescence method using Spectroscan MAX-G device. For the water area of the Kalamitsky Gulf, the features of the studied trace element spatial distribution are considered, groups of trace elements with different spatial distribution patterns are identified. The correlation analysis determined influence of physicochemical characteristics of the sediments on distribution and accumulation of trace elements. In the water area of the Kalamitsky Gulf, a statistically significant correlation was observed of nickel, iron and zinc content with the clay fraction; dependance of increased concentrations of zinc, nickel and chrome on the content of organic carbon and predominance of lead in highcarbonate sediments. For the Balaklava Bay, the maximum positive values of correlations with the pelitic-silty fraction were noted for iron, manganese, vanadium and chrome; organic carbon correlates with chrome, iron, nickel and copper, whereas carbonates correlate with strontium. Similar patterns were observed earlier in the Sevastopol region bays, Feodosiya Gulf and Kerch Strait.

scholarly journals Assessment of Water Quality of Major Tributaries in Seoul using Water Quality Index and Cluster Analysis

2020 ◽  
Vol 42 (10) ◽  
pp. 452-462
Author(s):  
Jinhyo Lee ◽  
Hyunju Ha ◽  
Manho Lee ◽  
Mokyoung Lee ◽  
Taeho Kim ◽  
...  
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
River Water ◽  
Quality Index ◽  
Water Quality Management ◽  
River Water Quality ◽  
Quality Information ◽  
Linkage Method ◽  
And Cluster Analysis

Objectives : 17 water quality measurement networks (WQMNs, tributaries) in Seoul were analyzed by using NSFWQI and cluster analysis to provide basic data for future river water quality management so that citizens could easily and comprehensively understand the water quality information on the rivers in Seoul.Methods : For the past 3 years (2015~2017), in order to estimate WQI, 9 items, DO (% sat), Fecal coliform, pH, BOD, Temperature change (TC), TP, NO3-, Turbidity and Total solids, were selected from among the 19 water quality data measurement items produced monthly from 17 WQMNs in Seoul. WQI was derived and graded using NSFWQI and cluster analysis was performed using Ward Linkage Method, SOM (Self Organizing Map).Results and Discussion : Water quality of most water quality monitoring networks was BOD Ⅱ grade (slightly good) or higher and TP Ⅲ grade (normal) or higher according to the standard of water quality and water ecosystem river living environment, and NSFWQI was also 64 (Medium)~89 (Good). All showed good water quality. NSFWQI does not show a significant difference by season, so it is believed that it is affected by anthropogenic sources rather than seasonal effects. As a result of examining the correlation between NSFWQI and water quality level according to environmental standards, it was confirmed that R2 has a relatively good correlation with 0.78, and there is no clear difference between the two groups, and through this, it was found that the currently implemented water quality rating system and NSFWQI are well matched. As a result of cluster analysis using ward linkage method and SOM for 17 WQMNs, it was largely divided into 6 groups according to water quality characteristics.Conclusions : It is important to manage pollution sources to systematically manage river water quality as a water resource. It is therefore expected that by converting from the complicated and various water quality information such as is found in this study into a simple water quality index and grouping, the river water quality can be easily understood and can be utilized in the future as basic data for water quality management in Seoul.

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