Water Pollution Assessment and Dredging Effects of the Nanfei River in China

2014 ◽  
Vol 1030-1032 ◽  
pp. 657-660
Author(s):  
Yu Feng Xie ◽  
Ya Zhang ◽  
Wan Jing Wang ◽  
Wen Jing Li
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Pollution Assessment ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Class Iii ◽  
Class V ◽  
N Removal ◽  
Data Dredging

This paper employed the water quality monitoring data of Nanfei River to analyze the water pollution status, and to contrast the water quality change of the river section which has been dredged. We selected TN, TP, COD and NH3-N as the evaluation factors to study the spatial distribution. Results showed that TN concentration was seriously higher than the upper limit of surface water standard class V, and TP and COD values were in class V, and NH3-N concentration was in class III. Refer to previous water quality monitoring data, dredging projects of Nanfei River worked significantly on TP, COD and NH3-N removal, while TN concentration was 5 times more than before.

scholarly journals THE EVALUATION OF WATER POLLUTION WITH THE HELP OF REMOTE SENSING TOOLS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 403-408
Author(s):  
P. Šádek ◽  
J. Struhár
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Water Quality ◽  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Pollution Assessment ◽  
Vegetation Period ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Landsat 8

<p><strong>Abstract.</strong> With the growing population, there is a growing demand for quality drinking water. Especially in developing parts of the world, this is a serious problem. The aim of this work is to test remote sensing methods for water quality monitoring. The presented part of the project is focused on introducing the process of water pollution assessment using vegetation indices, which are derived only using RGB images. Water quality monitoring is based on satellite imagery Landsat 8 and UAV images Phantom 3. As reference data was used in-site measurements in profiles points. In-site measurements were repeated every month in the vegetation period from April to September. Based on regression analysis, the equation for the calculation of the amount of chlorophyll and the statistical evaluation of the quality of these equations is derived for each vegetation index. The best results were achieved using the ratio aquatic vegetation index (RAVI) and ExG (Excess green) indices of 97% and 96.8% respectively.</p>

scholarly journals Water Physico-Chemical Characteristics of the Lakes Burera and Ruhondo, Rwanda

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Valens Habimana ◽  
Antoine Nsabimana
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chemical Characteristics ◽  
Class Iii ◽  
Populated Area ◽  
Physico Chemical ◽  
Steep Slopes ◽  
Extensive Agriculture

Rwanda possesses multiple lakes, whose properties were rarely described. The present study assessed physico-chemical characteristics of water in Burera and Ruhondo lakes located in highly populated area with steep slopes, which are under extensive agriculture, thus water quality monitoring is important. Both lakes were alkaline with high content of Mg, while Ruhondo had higher electrical conductivity than Burera. Phosphorus and nitrogen exceeded Class III EPA standards indicating that both lakes are at risk of eutrophication. Keywords: water quality, lakes Burera and Ruhondo, Rwanda

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