Water quality, sediment quality, and stream-channel classification of Rock Creek, Washington, D.C., 1999-2000

2002 ◽  
Keyword(s):  
Water Quality ◽  
Sediment Quality ◽  
Stream Channel ◽  
Channel Classification

Sewers and the Quality of Canals and Ditches in Amsterdam

1993 ◽  
Vol 27 (5-6) ◽  
pp. 61-67 ◽  
Author(s):  
E. Jacobs ◽  
J. W. van Sluis
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Sediment Quality ◽  
Water System ◽  
Water Systems ◽  
Sewer System ◽  
Water And Sediment ◽  
Combined Sewer ◽  
Water And Sediment Quality

The surface water system of Amsterdam is very complicated. Of two characteristic types of water systems the influences on water and sediment quality are investigated. The importance of the sewer output to the total loads is different for both water systems. In a polder the load from the sewers is much more important than in the canal basin. Measures to reduce the emission from the sewers are much more effective in a polder. The effect of these measures on sediment quality is more than the effect on water quality. Some differences between a combined sewer system and a separate sewer system can be found in sediment quality.

scholarly journals Classification Maps for TDS Concentrations in the GIS Along Euphrates River, Iraq

2021 ◽  
Vol 232 (8) ◽  
Author(s):  
Ali Chabuk ◽  
Zahraa Ali Hammood ◽  
Nadhir Al-Ansari ◽  
Salwan Ali Abed ◽  
Jan Laue
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
World Health Organization ◽  
World Health ◽  
Euphrates River ◽  
Poor Water Quality ◽  
Quality Category ◽  
Health Organization ◽  
Dissolved Salts

AbstractIraq currently undergoing the problem of water shortage, although Iraq has two Rivers (Euphrates and Tigris) pass throughout most of its areas, and they have represented a major source of water supply. In the current research, to evaluate the quality of the Euphrates river in Iraq based on the values of total dissolved salts (TDS), the TDS concentrations were collected from sixteen sections along the river in the three succeeding years (2011, 2012, and 2013). The evaluation of the river was done depending on the classification of (W.H.O. (World Health Organization). (2003). Total Dissolved Salts in Drinking-water: Background document for development of W.H.O. Guidelines for Drinking-water Quality. World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland). of rivers for drinking uses. Inverse Distance Weighting Technique (IDWT) as a tool in the GIS was employed to establish the maps of the river that using interpolation/prediction for the TDS concentrations to each selected year and the average values of TDS for these 3 years. Based on the five categories of rivers’ classification of the TDS concentrations according to the (W.H.O. (World Health Organization). (2003). Total Dissolved Salts in Drinking-water: Background document for development of W.H.O. Guidelines for Drinking-water Quality. World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland), the Euphrates river was classified, and the maps of classification for the years 2011, 2012 and 2013 and the average values for 3 years were created. The average values for 3 years of TDS along the Euphrates river indicated that the sections from SC-1 to SC-4 as moderate-water-quality-Category-3, the sections from SC-5 to SC-10 as poor-water-quality-Category-4, while the sections between SC-11 to SC-16 as very poor-water-quality-Category-5. The interpolation maps showed that the Euphrates river in Iraq was ranged from moderate water quality (Category-3) to very poor water quality (Category-5).

scholarly journals Classification of River Water Quality Using Multivariate Analysis

2015 ◽  
Vol 30 ◽  
pp. 79-84 ◽  
Author(s):  
Shah Christirani Azhar ◽  
Ahmad Zaharin Aris ◽  
Mohd Kamil Yusoff ◽  
Mohammad Firuz Ramli ◽  
Hafizan Juahir
Keyword(s):  
Water Quality ◽  
Multivariate Analysis ◽  
River Water ◽  
River Water Quality

An assessment of the impact of the proposed EU bathing water directive on Irish coastal bathing area compliance

2005 ◽  
Vol 51 (3-4) ◽  
pp. 225-230 ◽  
Author(s):  
R. Chawla ◽  
K. Real ◽  
B. Masterson
Keyword(s):  
Water Quality ◽  
Quality Standards ◽  
Water Quality Standards ◽  
Microsoft Excel ◽  
Microbial Water Quality ◽  
Bathing Water Directive ◽  
Bathing Water ◽  
Good Classification ◽  
The Impact

An assessment of the impact of the new microbial water quality standards of the proposed EU Bathing Water Directive on the classification of designated Irish coastal bathing areas is presented. The new standards are applied retrospectively to the microbial water quality results for the bathing seasons of 1999, 2000 and 2001, and the outcome is compared with that recorded under the present Bathing Water Directive. A Microsoft EXCEL application was developed to generate the retrospective bathing area classifications according to the proposed Directive (Excellent, Good, Poor). It was found that the number of Irish coastal bathing areas not attaining ‘Excellent’ classification (as would be required at present for the Blue Flag award) was trebled; the number attaining ‘Good’ classification was increased by about 50%, and the number attracting ‘Poor’ classification (equivalent to ‘Fail’ under the present Directive) was increased nine-fold. Some of the shortcomings of the proposed Directive and suggestions for its revision are discussed.

scholarly journals TO THE QUESTION OF COMPREHENSIVE ASSESSMENT OF SURFACE WATER IN THE CENTRAL CAUCASUS DURING WINTER BETWEEN AND SUMMER FLOOD

2021 ◽  
Vol 6 (5(55)) ◽  
pp. 22-27
Author(s):  
Liliya Zuberovna Zhinzhakova ◽  
Elena Alexandrovna Cherednik
Keyword(s):  
Water Quality ◽  
Surface Waters ◽  
Inorganic Nitrogen ◽  
High Water ◽  
Polluted Water ◽  
Trace Impurities ◽  
Central Caucasus ◽  
Summer Flood

Based on long-term observations, the assessment of the pollution of the rivers of the Central Caucasus was carried out by comparing the calculated coefficients of the complexity of pollution and the specific combinatorial index of pollution of surface waters, and the quality classes were determined. The results of chemical analysis of the concentration levels of trace impurities (Mo, Pb, Zn, V, Ni, Cr, Mn, Ag) and inorganic nitrogen compounds (NO2 -, NO3 — and NH4 +) in the waters of 13 rivers in two permanent sections of each watercourse were used. The results of calculating the indicators of pollution in the waters of rivers of winter low water and summer high water are presented. The most polluted watercourses and the frequency of pollution in each river are identified, estimated by the values of the specific combinatorial index of water pollution, the coefficient of complexity of pollution, and the class of water quality is presented. The assessment of the most polluted water bodies during the winter low-water period and summer flood is given according to the classification of water quality. According to long-term observations, the features of watercourses and their differences in terms of pollution are presented.

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

scholarly journals Variations in selected water quality variables and metal concentrations in the sediment of the lower Olifants and Selati rivers, South Africa

Koedoe ◽  
1994 ◽  
Vol 37 (2) ◽  
Author(s):  
T. Seymore ◽  
H.H. Du Preez ◽  
J.H.J. Van Vuren ◽  
A. Deacon ◽  
G. Strydom
Keyword(s):  
Water Quality ◽  
Sediment Quality ◽  
Metal Concentrations ◽  
Water And Sediment ◽  
Water And Sediment Quality ◽  
Special Concern ◽  
Olifants River ◽  
Western Side ◽  
Physical And Chemical

A survey of the water and sediment quality of the lower Olifants River and lower Selati River was carried out. Metal concentrations (Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) in the water and sediment, as well as the physical and chemical characteristics of the water were determined over a two-year period (April 1990 - February 1992). The water quality of the lower Selati River, which flows through the Phalaborwa area, was found to be influenced by the mining and industrial activities in the area. It was also the case with the lower Olifants River after the Selati-Olifants confluence, although the concentrations of most variables did decrease from the western side of the Kruger National Park to the eastern side due to dilution of the water by tributaries of the Olifants River. Variables of special concern were sodium, fluoride. chloride, sulphate, potassium, the total dissolved salts and the metal concentrations (except strontium). The water quality of the Selati River in the study area is a great cause of concern and a further degradation thereof cannot be afforded.

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

2008 ◽  
Vol 34 (6) ◽  
pp. 782-790 ◽  
Author(s):  
Manuel Alvarez-Guerra ◽  
Cristina González-Piñuela ◽  
Ana Andrés ◽  
Berta Galán ◽  
Javier R. Viguri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sediment Quality ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
Self Organizing
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