Analysis of Factors Affecting the Water Quality of the Namhan River - Focusing on Continuous Causal Relationship between Up and Down streams -

2020 ◽  
Vol 20 (2) ◽  
pp. 17-24
Author(s):  
Youngki Jung ◽  
◽  
Chulhwi Park ◽  
Jongsun Choi ◽  
Yoonmin Kang
Keyword(s):  
Water Quality ◽  
Causal Relationship ◽  
Factors Affecting

scholarly journals Factors affecting lean, wet-season water quality of Tilaiya reservoir in Koderma District, India during 2013–2017

Water Science ◽  
2020 ◽  
Vol 34 (1) ◽  
pp. 85-97
Author(s):  
Arun Kumar Pramanik ◽  
Deepanjan Majumdar ◽  
Abhik Chatterjee
Keyword(s):  
Water Quality ◽  
Wet Season ◽  
Factors Affecting

Ecological Features of a Country House Water Supply

2019 ◽  
Vol 23 (2) ◽  
pp. 68-71
Author(s):  
G.I. Zubareva
Keyword(s):  
Water Quality ◽  
Water Supply ◽  
Optimal Choice ◽  
Comparative Assessment ◽  
Country House ◽  
Factors Affecting ◽  
Ecological Features ◽  
Quality Of Water ◽  
Over Time

Considered the most frequently used sources of autonomous water supply of a country house – manhole and well. It is shown that the chemical composition of natural water in these structures does not meet the requirements of SaNPiN 2.1.4.1175-02 and varies over time. The reasons for the change in the water quality in the manhole and the well and the ways to solve the problem are listed. The factors affecting the water quality in the manhole and the well are indicated – the characteristic of the water of the aquifer and the arrangement of the structures. The conclusion was made that it is impossible to make the optimal choice between a manhole or a well for the environmentally acceptable water supply of a country house for the quality of water in each of the water intakes. A comparative assessment of the manhole and the well is given for a number of indicators in order to optimize the choice between them.

scholarly journals The water quality of streams located in large urban agglomeration, case study of the SłuŻewiecki Stream

2018 ◽  
Vol 44 ◽  
pp. 00193 ◽  
Author(s):  
Małgorzata Wojtkowska ◽  
Magdalena Matula ◽  
Paulina Kozińska
Keyword(s):  
Water Quality ◽  
Field Research ◽  
Capital City ◽  
Factors Affecting ◽  
Urbanized Area ◽  
Poor Water Quality ◽  
The Subject ◽  
The One

The quality assessment of watercourse, located in a highly urbanized area was the goal of research. The SłuŻewiecki Stream, which flows in the southern part of the capital city of Warsaw is the subject of study. The aim of the research was to determine the tendency of the water quality changes in the SłuŻewiecki Stream, flowing in the highly urbanized area and being a sink of the non-point and industry sources of pollutions as well as the surface runoffs which comes from its catchment. The field research was conducted along the entire length of the stream in 2015 and 2016. The water samples were collected monthly from a depth to 20 cm. Water quality tests were carried out in accordance with the valid analytical methods. The obtained results point to poor water quality. Therefore it can be conducted that the development and use of the direct catchment is the one of main factors affecting the water quality in SłuŻewiecki Stream. The tributaries, often not inventoried, from built-up, populated areas, are the most polluted. Tributaries contain large amount of organic compounds and nutrients.

scholarly journals Recent (2003-05) water quality of Barton Springs, Austin, Texas, with emphasis on factors affecting variability

2006 ◽  
Author(s):  
Barbara Mahler ◽  
Bradley D. Garner ◽  
MaryLynn Musgrove ◽  
Amber L. Guilfoyle ◽  
Mohan V. Rao
Keyword(s):  
Water Quality ◽  
Factors Affecting ◽  
Barton Springs

Assessment of the spatial–temporal variations on the water quality of stagnant Haihe River, Tianjin, North China

2017 ◽  
Vol 18 (3) ◽  
pp. 1103-1116
Author(s):  
Zhiwei Zhang ◽  
Ling Xiao ◽  
Min Ji ◽  
Can Wang
Keyword(s):  
Water Quality ◽  
Control Strategies ◽  
Principal Component ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Anthropogenic Factors ◽  
Haihe River ◽  
Factors Affecting ◽  
And Cluster Analysis

Abstract Spatial–temporal variations in 13 selected water quality parameters from four stations located in the stagnant Haihe River from 2012 to 2014 were analysed. Principal component analysis and cluster analysis were applied. The main latent anthropogenic factors affecting the water quality of Sanchakou, Sixin Bridge, Liulin, and Erdao Gate were combined sewer overflow, organic matter, domestic sewage, and agricultural diffuse source, respectively. External inputs mainly affected quality water in the summer–autumn season. By contrast, intrinsic biochemical processes were highly correlated with water quality in the winter–spring season. Ranges of total nitrogen (TN) and total phosphorus (TP) of four sampling sites measured 1.2 mg/L to 11.4 mg/L and 0.04 mg/L to 2.06 mg/L, respectively. TN/TP (mass ratio) was mainly between 9 and 23, indicating severely eutrophicated mainstream of the Haihe River and sufficient amounts of nutrients for phytoplankton growth and reproduction. Hence, dual nutrients control strategies should be implemented in this stagnant urban river.

scholarly journals Using a gray multivariate model to predict impacts on the water quality of the Zhanghe River in China

2021 ◽  
Author(s):  
Feifei Fan ◽  
Zhengran Qiao ◽  
Lifeng Wu
Keyword(s):  
Water Quality ◽  
Social Factors ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Order ◽  
Swarm Optimization ◽  
Factors Affecting ◽  
The Social ◽  
Convolution Model ◽  
Potential Impact

Abstract In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative gray multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using COD as the water quality indicator).

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