Estimation of the measurement uncertainty, including the contribution arising from sampling, of water quality parameters in surface waters of the Loire-Bretagne river basin, France

2020 ◽  
Vol 25 (4) ◽  
pp. 281-292
Author(s):  
Nathalie Guigues ◽  
Bénédicte Lepot ◽  
Michèle Desenfant ◽  
Jacky Durocher
Keyword(s):  
Water Quality ◽  
Measurement Uncertainty ◽  
River Basin ◽  
Surface Waters ◽  
Quality Parameters ◽  
Water Quality Parameters

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia

2014 ◽  
Vol 187 (1) ◽  
Author(s):  
Ibrahim Mohamed ◽  
Faridah Othman ◽  
Adriana I. N. Ibrahim ◽  
M. E. Alaa-Eldin ◽  
Rossita M. Yunus
Keyword(s):  
Water Quality ◽  
Multivariate Analysis ◽  
River Basin ◽  
Quality Parameters ◽  
Water Quality Parameters

scholarly journals Assessing the Impact of Land Use on Water Quality across Multiple Spatial Scales in U-tapao River Basin, Thailand

2015 ◽  
Vol 20 (2) ◽  
pp. 54-60 ◽  
Author(s):  
S. Gyawali ◽  
K. Techato ◽  
S. Monprapusson
Keyword(s):  
Water Quality ◽  
Land Use ◽  
River Basin ◽  
Spatial Scales ◽  
Buffer Zone ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Geographical Information ◽  
Land Uses ◽  
The Impact

The study investigated the linkages between land uses and water quality in U-tapao river basin, Thailand, in order to examine the impact of land use changes on full -basin, sub-watershed and buffer zone scales (1000m, 500m and 200m) on river water quality through Geographical Information Systems (GIS) and statistical analyses. Correlation and regression analysis were applied for ten water quality parameters. In scale analysis, in the most cases, the sub-watershed scale showed the clear relationship between land use water quality rather than full-basin and buffer zone scales. This indicates that the level of relationship between land use and water quality depends upon scale therefore the relationship between water quality parameters and land uses should be studied in multiple scales and it helps to develop effective river basin management in future.Journal of Institute of Science and Technology, 2015, 20(2): 54-60

Water quality parameters controlling the photodegradation of two herbicides in surface waters of the Columbia Basin, Washington

Chemosphere ◽  
2013 ◽  
Vol 93 (9) ◽  
pp. 1734-1741 ◽  
Author(s):  
Olha S. Furman ◽  
Miao Yu ◽  
Amy L. Teel ◽  
Richard J. Watts
Keyword(s):  
Water Quality ◽  
Surface Waters ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Columbia Basin

scholarly journals Implementation of Machine Learning Methods for Monitoring and Predicting Water Quality Parameters

2020 ◽  
Vol 11 (2) ◽  
pp. 9285-9295 ◽  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Learning Tools ◽  
Water Quality Data

The importance of good water quality for human use and consumption can never be underestimated, and its quality is determined through effective monitoring of the water quality index. Different approaches have been employed in the treatment and monitoring of water quality parameters (WQP). Presently, water quality is carried out through laboratory experiments, which requires costly reagents, skilled labor, and consumes time. Thereby making it necessary to search for an alternative method. Recently, machine learning tools have been successfully implemented in the monitoring, estimation, and predictions of river water quality index to provide an alternative solution to the limitations of laboratory analytical methods. In this study, the potentials of one of the machine learning tools (artificial neural network) were explored in the predictions and estimation of the Kelantan River basin. Water quality data collected from the 14 stations of the River basin was used for modeling and predicting (WQP). As for WQP analysis, the results obtained from this study show that the best prediction was obtained from the prediction of pH. The low kurtosis values of pH indicate that the appearance of outliers give a negative impact on the performance. As for WQP analysis for each station, we found that the WQP prediction in station 1, 2, and 3 give the good results. This is related to the available data of those stations that are more than the available data in other stations, except station 8.

scholarly journals Modeling water quality parameters in the surface waters of Samanlı and Safran Rivers, Turkey

2021 ◽  
Vol 1 (1) ◽  
pp. 61-84
Author(s):  
Jülide HIZAL YÜCESOY ◽  
Nergiz Kanmaz ◽  
Osman Koçal ◽  
Jülide Hızal Yücesoy
Keyword(s):  
Water Quality ◽  
Surface Waters ◽  
Quality Parameters ◽  
Water Quality Parameters

scholarly journals Effect of rainfall seasonality and land use on the water quality of the paraíba do sul river

2021 ◽  
Vol 29 ◽  
pp. 211-228
Author(s):  
Dayane Andrade da Silva Bourguignon ◽  
Micael de Souza Fraga ◽  
Gustavo Bastos Lyra ◽  
Roberto Avelino Cecílio ◽  
Marcel Carvalho Abreu
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Paraíba Do Sul River ◽  
Dry And Rainy Seasons

Monitoring water quality is important for the suitable management of water resources. Therefore, this study aims to assess the main water quality parameters and the National Sanitation Foundation-Water Quality Index (WQINSF) of four locations on the Paraíba do Sul River basin, in the state of Rio de Janeiro, influenced by different land use and land cover, and in the dry and rainy seasons. The following quality parameters were evaluated: total phosphorus (TP), nitrate (NO3-), dissolved oxygen (DO), potential of hydrogen (pH), turbidity (Turb), thermotolerant coliforms (Col), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Twater) and air temperature (Tair). Statistical differences (p < 0.05) were observed between the dry and rainy seasons for the parameters: TP, Col, Turb, TDS, Twater, Tair, NO3-, DO, and WQINSF. The concentration of rainfall was effective in water quality parameters behavior. WQINSF was lower in the rainy season and possibly the runoff was the major cause of water quality degradation. Land use and land cover influenced the concentration of DO and Col and, consequently, WQINSF. Despite statistical differences, in most cases, the Paraíba do Sul River basin lies in medium water quality index according to the classification of the National Water and Sanitation Agency (ANA).

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