scholarly journals Hydrochemical water quality monitoring of natural water bodies of the Ural river basin

2021 ◽  
pp. 358-368
Author(s):  
O. V. Atamanova ◽  
E. I. Tikhomirova ◽  
V. A. Burahta ◽  
L. I. Baytlesova ◽  
A. К. Dzhubayalieva
Keyword(s):  
Water Quality ◽  
Fresh Water ◽  
River Basin ◽  
Drainage Basin ◽  
General Characteristic ◽  
Water Quality Monitoring ◽  
Water Bodies ◽  
Hydrochemical Monitoring ◽  
Flood Period ◽  
Residential Use

A general characteristic of the river basin of the interstate Ural river and information on the economic use of the Ural river in its upper, middle and lower reaches are given. Information on the hydrology of the liquid and solid flow of the Ural river is presented. Hydrochemical monitoring of water bodies of the Ural river basin was conducted during the flood period, during the summer low water period and during the autumn low water period of 2017–2018. The hydrochemical monitoring of the water quality in natural reservoirs of the Ural drainage basin made it possible to identify an excess of the MPCs for residential use and for fishery of heavy metal ions in the water of the examined reservoirs. An excess of cadmium ions by (1.2–1.4) MPC for residential use was found in the Ilek river during its low water as well as in the Ural river near the border with the Russian Federation during the flood period in the amount of (3.1–3.4) MPC for residential use and near the village Zharsuat in the period of summer and autumn low water in the amount of (1.5–2.6) MAC for residential use. An excessive concentration of lead ions by (1.2–1.4) of MPC for fresh water fisheries in the middle and lower reaches of the Ural river at different times of the year was found. Excessive concentrations of ions of different heavy metals in comparison with their MPC for fresh water fisheries were found in all water bodies in different periods of the year. The greatest excess of zinc ions in comparison with its MPC for fresh water fisheries was observed during low water.

scholarly journals Transformation of water supply of population and economic objects in the Don River basin for the period of 1990-2018

2020 ◽  
Vol 163 ◽  
pp. 05001
Author(s):  
Alexander Demin ◽  
Anna Zaitseva
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Supply ◽  
Fresh Water ◽  
River Basin ◽  
Water Bodies ◽  
Drinking Water Supply ◽  
Water Complex ◽  
Current State ◽  
Don River

The analysis of the current state of major water consumers and water complex participants is performed. The data on the increase in centralized water supply of the residential accommodation during 2000-2018 is presented. Significant decrease in the volume of water used for drinking water supply is shown. The improvement of water quality in water bodies of most regions is revealed. The use of fresh water for production needs in the Don River basin decreased from 5.8 to 2.9 km3 from 1990 to 2018. The water circulation coefficient in industry increased from 64 to 85%. The area of irrigated lands in the basin has started to decrease significantly since 1990. In 2000 every second hectare of available irrigated land was watered and in 2016 only every fourth was watered.

Outflows of Organic Halide Precursors from Forest Regions

1989 ◽  
Vol 21 (12) ◽  
pp. 1877-1880 ◽  
Author(s):  
S. Saito ◽  
K. Hattori ◽  
T. Okumura
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Surface Soil ◽  
Soil Layer ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Deep Soil ◽  
Organic Halide ◽  
Forest Regions ◽  
Surface Soil Layer

Outflows of organic halide precursors (OXPs) from forest regions were studied in relation to water quality monitoring in the Yodo River basin. Firstly, the contribution of outflows from forest regions relative to the total was roughly estimated. Then equations for flows of these substances were formulated, divided into four different subflow categories: precipitation; throughfall; surface soil layer; and, deep soil layer. Finally, annual outflow loads were calculated for a test forest area.

Development of Dialog System Model for Eutrophication Control between Discharging River Basin and Receiving Water Body – Case Study of Lake Sagami (Japan)

1989 ◽  
Vol 21 (12) ◽  
pp. 1821-1824
Author(s):  
M. Suzuki ◽  
K. Chihara ◽  
M. Okada ◽  
H. Kawashima ◽  
S. Hoshino
Keyword(s):  
Water Management ◽  
Water Quality ◽  
Expert System ◽  
River Basin ◽  
Water Body ◽  
Water Bodies ◽  
Quality Data ◽  
Pollution Load ◽  
Water Quality Data ◽  
Receiving Water

A computer program based on expert system software was developed and proposed as a prototype model for water management to control eutrophication problems in receiving water bodies (Suzuki etal., 1988). The system has several expert functions: 1. data input and estimation of pollution load generated and discharged in the river watershed; 2. estimation of pollution load run-off entering rivers; 3. estimation of water quality of receiving water bodies, such as lakes; and 4. assisting man-machine dialog operation. The program can be used with MS-DOS BASIC and assembler in a 16 bit personal computer. Five spread sheets are utilized in calculation and summation of the pollutant load, using multi-windows. Partial differential equations for an ecological model for simulation of self-purification in shallow rivers and simulation of seasonal variations of water quality in a lake were converted to computer programs and included in the expert system. The simulated results of water quality are shown on the monitor graphically. In this study, the expert system thus developed was used to estimate the present state of one typical polluted river basin. The river was the Katsura, which flows into Lake Sagami, a lake dammed for water supply. Data which had been actually measured were compared with the simulated water quality data, and good agreement was found. This type of expert system is expected to be useful for water management of a closed water body.

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

Multi-parametric water quality monitoring approach according to the WFD application in Evros trans-boundary river basin: priority pollutants

Desalination ◽  
2008 ◽  
Vol 226 (1-3) ◽  
pp. 306-320 ◽  
Author(s):  
Anastasia D. Nikolaou ◽  
Sureyya Meric ◽  
Demetris F. Lekkas ◽  
Vincenzo Naddeo ◽  
Vincenzo Belgiorno ◽  
...  
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Priority Pollutants

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

Monitoring and modelling mining impacts on water quality in Chindwin River Basin, Myanmar

2020 ◽  
Author(s):  
Thanapon Piman ◽  
Chayanis Krittasudthacheew ◽  
Shakthi K. Gunawardanaa ◽  
Sangam Shresthaa
Keyword(s):  
Water Quality ◽  
Heavy Metal ◽  
Drinking Water ◽  
Water Treatment ◽  
River Basin ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Mining Activities

<p>The Chindwin River, a major tributary of the Ayeyarwady River in Myanmar, is approximately 850 km long with a watershed area of 115,300 km<sup>2</sup>. The Chindwin River is essential for local livelihoods, drinking water, ecosystems, navigation, agriculture, and industries such as logging and mining. Over the past two decades, Myanmar’s rapid economic development has resulted in drastic changes to socio-economic and ecological conditions in the basin. Water users in the basin reported that there is a rapid extension of gold and jade mining and they observed a noticeable decline in water quality along with increased sedimentation and turbidity. So far, however, Myanmar has not undertaken a comprehensive scientific study in the Chindwin River Basin to assess water quality and sources of water pollution and to effectively address issues of river basin degradation and concerns for public health and safety. This study aims to assess the status of water quality in the Chindwin River and the potential impact of mining activities on the water quality and loading through monitoring program and modeling approach. 17 locations in the upper, middle and lower parts of the Chindwin River Basin were selected for water quality monitoring. These sites are located near Homalin, Kalewa, Kani and Monywa townships where human activities and interventions could affect water quality. Water quality sampling and testing in the Chindwin River was conducted two times per year: in the dry season (May-June) and in the wet season (September-October) during 2015-2017. We monitored 21 parameters including heavy metals such as Lead (Pb), Mercury (Hg), Copper (Cu) and Iron (Fe). The observed values of Mercury in Uru River in the upper Chindwin River Basin which located nearby gold mining sites shown higher than the WHO drinking standard. This area also has high values of turbidity and Total Suspended Solid. The SHETRAN hydrological model, PHREEQC geochemical model and LOADEST model were used to quantify the heavy metal loads in the Uru River. Results from scenario analysis indicate an increase in Arsenic and Mercury load under increment of concentration due to expansions in mining areas. In both baseline and future climate conditions, the Uru downstream area shows the highest load effluent in both Arsenic and Mercury. These heavy metal loads will intensify the declining water quality condition in Chindwin River and can impact negatively on human health who use water for drinking. Therefore, we recommend that water quality monitoring should continue to provide scientific-evidence for decision-makers to manage water quality and mining activities properly.  Water treatment systems for drinking water are required to remove turbidity, Total Suspended Solid, and Mercury from raw water sources. Raising awareness of relevant stakeholders (local people, farmers, private sectors, etc.) is necessary as many people living in the Chindwin River Basin are using water directly from the river and other waterways without proper water treatment.</p>

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