Impact of non-detects in water quality data on estimation of constituent mass loading

2002 ◽  
Vol 45 (9) ◽  
pp. 219-225 ◽  
Author(s):  
M. Kayhanian ◽  
A. Singh ◽  
S. Meyer
Keyword(s):  
Water Quality ◽  
Data Analysis ◽  
Censored Data ◽  
Quality Data ◽  
Data Sets ◽  
Mass Loading ◽  
Water Quality Data ◽  
Mean Values ◽  
Constituent Mass

Often, fractions of stormwater constituents are not detected above laboratory reporting limits and are reported as non-detect (ND), or censored data. Analysts and stormwater modelers represent these NDs in stormwater data sets using a variety of methods. Application of these different methods results in different estimates of constituent mean concentrations that will, in turn, affect mass loading computations. In this paper, different methods of data analysis were introduced to determine constituent mean concentrations from water quality datasets that include ND values. Depending on the number of NDs and the method of data analysis, differences ranging from 1 to 70 percent have been observed in mean values. Differences in mean values were, as shown by simulation, found to have significant impacts on estimations of constituent mass loading.

scholarly journals Water Quality Database Offers New Tools to Study Aquatic Systems

Eos ◽  
2017 ◽  
Author(s):  
Lily Strelich
Keyword(s):  
Water Quality ◽  
Aquatic Systems ◽  
Quality Data ◽  
Data Sets ◽  
Web Portal ◽  
Water Quality Data

Researchers assess the federal Water Quality Portal, a Web portal that unites disparate water quality data sets and resources.

scholarly journals Water Quality Data Analysis for Kanhan River

2014 ◽  
Vol 9 (2) ◽  
pp. 447-455
Author(s):  
Snehal Kamble ◽  
P Nagarnaik ◽  
R Shrivastava
Keyword(s):  
Water Quality ◽  
Data Analysis ◽  
Quality Data ◽  
Water Quality Data

scholarly journals Characterizing water quality monitoring visualization with Hadoop and Google Maps

2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Large Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Data Sets ◽  
Google Maps ◽  
Water Quality Data ◽  
Computer Clusters

Abstract In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.

COMPUTER ASSISTED WATER QUALITY DATA ANALYSIS

1984 ◽  
Vol 1 (1) ◽  
pp. 48-52
Author(s):  
Michael W. Mullen ◽  
Stephen R. Smith ◽  
Richard E. Price ◽  
Terry S. Smith
Keyword(s):  
Water Quality ◽  
Data Analysis ◽  
Quality Data ◽  
Computer Assisted ◽  
Water Quality Data

scholarly journals Utilisation of Maqalika Reservoir as a source of potable water for Maseru city in Lesotho

2005 ◽  
Author(s):  
◽  
Masupha Letsie
Keyword(s):  
Water Quality ◽  
National Standards ◽  
Quality Data ◽  
Ungauged Catchments ◽  
Water Quality Data ◽  
Mean Values ◽  
Run Off ◽  
Remaining Capacity ◽  
The Impact

Lesotho is a land locked country, entirely surrounded by the Republic of South Africa. Maseru is the capital of Lesotho and the country’s main centre for commerce and industry. The study area is located on the North-Eastern outskirts of the Maseru urban area. The catchment occupies an area of 44km2 with a length of about 13 km and channel slope of 0.4 km/km. The Maqalika Reservoir was built in 1983 to meet the water demands for Maseru city up to 1995, and its storage capacity was 3.7 Mm3. The storage is gradually decreasing as sediment, carried by the natural run-off accumulates in the reservoir. Moreover, water pumped into the reservoir from the Caledon River (which is heavily sedimented) adds its own contribution of silt. The reservoir is located in a very densely populated area, and is heavily polluted leading to high purification costs. The study was motivated by the fact that Welbedacht Dam was constructed in 1973 in the Caledon catchment but downstream of Maqalika. After 20 years, 85% of the volume of the dam was silted. The study was intended in finding whether the positioning of the Maqalika reservoir is acceptable and to find its remaining capacity as a water body supplying a fast growing city. Consideration was also given to the effect of land use practices on the water quality of the Maqalika reservoir, including the cost incurred during purification. The water quality data on physico- chemical was collected from the Water and Sewerage Authority and was analysed using excel spreadsheets. Results obtained were compared with WHO, SABS and National Standards of Lesotho. It was found that nitrates, phosphates and faecal coliforms levels were by far above minimum standards rendering water to be very contaminated and the source being leaking sewers, defeacation in dongas and leachate from Tsosane and Lower Thamae dumping site. Iron levels were also high with mean values beyond 0.3mg/l and the source being leachate from dumping sites, poor disposal of scraps and minerals from soil. Conductivity levels were high and the suspected source is waste solid disposal having a maximum of 442mS/m in March 2001. Hardness, temperature and alkalinity do not pose much danger to Maqalika water since recorded results were almost within limits. Turbidity levels were very high and the main source was found to be catchment sedimentation through run-off. For determination of the impact of sedimentation through pumping, hydrological data was obtained from the Department of Water Affair (DWA) and analysed using Excel spreadsheets to get sediment concentrations. A linear regression graph was plotted using discharge against sediment concentration that yielded y = 0.0007x – 0.0019. This was used in the Rooseboom mathematical equation for estimation of volume occupied by sediment from 1983 - 2002 and was found to be 6789 m3. For determination of the impact due to catchment run-off, a map method of estimating sedimentation from ungauged catchments developed by Rooseboom was used and a volume of 4.598 x 106 m3 was obtained showing that the main contributor of sedimentation in the reservoir is catchment run-off. The chemical costs employed during purification were also compared between WASA and Umgeni Water of Kwazulu- Natal and WASA was found to be expensive with 9 cents/kl while Umgeni spent only 5.24 cents/kl.

scholarly journals Evaluation of Temporal and Spatial Variations of Water Quality Parameters in Zohreh River, Iran

2019 ◽  
Vol 6 (2) ◽  
pp. 75-82
Author(s):  
Maryam Ravanbakhsh ◽  
Yaser Tahmasebi Birgani ◽  
Maryam Dastoorpoor ◽  
Kambiz Ahmadi Angali
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Sodium Absorption ◽  
Data Sets ◽  
Water Quality Data ◽  
Data Set ◽  
Temporal And Spatial

Discriminant analysis (DA) and principal component analysis (PCA), as multivariate statistical techniques, are used to interpret large complex water quality data and assess their temporal and spatial variation in the basin of the Zohreh river. In this study, data sets of 16 water quality parameters collected from 1966 to 2013) in 4 stations (1554 observations for each parameter) were analyzed. PCA for data sets of Kheirabad, Poleflour, Chambostan and Dehmolla stations resulted in 4, 4, 4, and 3 latent factors accounting for 88.985%, 93.828%, 88.648%, and 88.68% of the total variance in water quality parameters, respectively. It is indicated that total dissolved solids (TDS), electrical conductivity (EC), chlorides (Cl−), sodium (Na), sodium absorption ratio (SAR), and %Na were responsible for water quality variations which are mainly related to natural and anthropogenic pollution sources including climate effects, gypsum, and salt crystals in the supratidal of Zohreh river delta, fault zones of Chamshir I and II, drainage of sugarcane fields, and domestic and industrial wastewaters discharge into the river. DA reduced the data set to only seven parameters (discharge, temperature, electrical conductivity, HCO3-, Cl-, %Na, and T-Hardness), affording more than 58.5% correct assignations in temporal evaluations and describing responsible parameters for large variations in the quality of the Zohreh river.

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