Assessment of Water Quality in the Elbe River at Low Water Conditions Based on Factor Analysis

Baborowski, Martina; Büttner, Olaf; Einax, Jürgen W.

doi:10.1002/clen.201000373

Cited by 23 publications

(16 citation statements)

References 24 publications

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“…Sulfate (SO 2− 4 ) and chloride (Cl − ) were analysed by suppressed conductivity using an ICS-3000 ion chromatography system (Dionex, Idstein, Germany) and automatically generated potassium hydroxide eluent. Concentrations of Ca, Mg, Na, K, Al, Fe, and Mn were determined by optical emission spectroscopy with inductively coupled plasma (ICP-OES, Perkin-Elmer, OPTIMA 3000, Germany) (Baborowski, et al, 2011). Acidity and alkalinity were measured by an automatic titrator (Metrohm, Germany).…”

Section: Data Availabilitymentioning

confidence: 99%

A practical approach to lake water density from electrical conductivity and temperature

Moreira

Schultze

Rahn

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Density calculations are essential to study stratification, circulation patterns, internal wave formation and other aspects of hydrodynamics in lakes and reservoirs. Currently, the most common procedure is the use of CTD (conductivity, temperature and depth) profilers and the conversion of measurements of temperature and electrical conductivity into density. In limnic waters, such approaches are of limited accuracy if they do not consider lake-specific composition of solutes, as we show. A new approach is presented to correlate density and electrical conductivity, using only two specific coefficients based on the composition of solutes. First, it is necessary to evaluate the lake-specific coefficients connecting electrical conductivity with density. Once these coefficients have been obtained, density can easily be calculated based on CTD data. The new method has been tested against measured values and the most common equations used in the calculation of density in limnic and ocean conditions. The results show that our new approach can reproduce the density contribution of solutes with a relative error of less than 10 % in lake waters from very low to very high concentrations as well as in lakes of very particular water chemistry, which is better than all commonly implemented density calculations in lakes. Finally, a web link is provided for downloading the corresponding density calculator.

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Section: Data Availabilitymentioning

confidence: 99%

A practical approach to lake water density from electrical conductivity and temperature

Moreira

Schultze

Rahn

et al. 2016

Hydrol. Earth Syst. Sci.

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“…This improvement is probably due to increased water levels in lakes due to snowmelt and precipitation [42]. Another study also showed the effect of seasonal fluctuations on the river water quality [43].…”

Section: Cluster Analysismentioning

confidence: 99%

“…The presence of CHL-a can be enhanced by higher amounts of TP; thus PC-3 can also be considered as an indicator of biological activities like PC-2. Baborowski et al [43] observed that concentration of CHL-a is directly related to the development of phytoplankton using factor analysis technique. The potential source of Ca is the sedimentary bedrock.…”

Section: Principal Component Analysismentioning

confidence: 99%

A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

Akbar

Hassan

Achari

2011

CLEAN Soil Air Water

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In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988-2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern-match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain $88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll-a. We obtained five clusters for the period 1994-1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters.

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“…For each principal component, component loadings indicate the correlation between the original variables and the newly formed components, which can be used to determine the relative importance of a parameter compared to others in a PC rather than the importance of the component itself [54][55][56]. From Tab.…”

Section: Parameters Related To the Variation Of Water Quality Betweenmentioning

confidence: 99%

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Fan

Cui

Zhang

et al. 2012

CLEAN Soil Air Water

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In the Pearl River Delta (PRD), river water quality deteriorates continually due to the population increase and ongoing industrialization and urbanization. In this study, a water quality management paradigm based on the seasonal variation is proposed. For better exploring the seasonal change of water quality, wavelet analysis was used to analyze the division of dry and wet seasons in the PRD during 1952PRD during -2009. Then water quality seasonal variation in 2008 and relevant impact factors were analyzed by multivariate statistic methods as a case to make some management measures. The results show that there are some differences of dry and wet seasons division among different years. Wet season mainly appear from April to September, which occupy the largest proportion among the 58 years (about 70%) and then followed by the wet season from May to October (about 13.8% of the total years). As to the water quality of 2008, significant differences exist between dry and wet seasons for 17 water quality parameters except TP, NO À 3 , Fe 2þ , and Zn 2þ . Levels of parameters pH, EC, COD Mn , BOD 5 , NH þ 4 , SO 2À 4 , and Cl À in dry season are much higher than those in wet season. In dry season the variations of river water quality are mainly influenced by domestic sewage, industrial effluents, and salt water intrusion. While in wet season, except the aforementioned pollution sources, drainages from cultivated land and livestock farm are also the main factors influencing water pollution. Thus, water quality management measures are proposed in dry and wet seasons, respectively. The results obtained from this study would further facilitate water quality protection and water resources management in the PRD.

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Assessment of Water Quality in the Elbe River at Low Water Conditions Based on Factor Analysis

Cited by 23 publications

References 24 publications

A practical approach to lake water density from electrical conductivity and temperature

A practical approach to lake water density from electrical conductivity and temperature

A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

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