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

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.

1995 ◽  
Vol 5 (3) ◽  
pp. 257-264 ◽  
Author(s):  
Hui Lin ◽  
Fuxiang Xia

Eos ◽  
2017 ◽  
Author(s):  
Lily Strelich

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


2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng

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.


1984 ◽  
Vol 1 (1) ◽  
pp. 48-52
Author(s):  
Michael W. Mullen ◽  
Stephen R. Smith ◽  
Richard E. Price ◽  
Terry S. Smith

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

2005 ◽  
Author(s):  
◽  
Masupha Letsie

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.


1997 ◽  
Vol 36 (5) ◽  
pp. 337-348 ◽  
Author(s):  
Paul H. Whitfield ◽  
Kathleen Dohan

Two wavelet transform techniques for identifying water quality transients are applied to example data sets from two small streams. Temperature and conductance represent the range of properties from periodic processes to transient events. Both methods were successful in identifying the location, duration and magnitude of the transient events in these data sets. The methods may be refined to automate the detection and classification of transient events.


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