Water Quality Data Analysis And Interpretation Pdf
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- INTERPRETATION OF WATER QUALITY DATA BY PRINCIPAL COMPONENTS ANALYSIS
- Water quality
- Graphical interpretation of water quality data
This online course was developed to introduce tools for water quality data visualization and statistical analysis using R , provide exercises to gain experience with real data to analyse for effects, trends, etc. Introduce tools for water quality data visualization and analysis R and R Studio; open source statistical software. Run through hands-on activities with real data to analyse for effects, trends, etc.
INTERPRETATION OF WATER QUALITY DATA BY PRINCIPAL COMPONENTS ANALYSIS
Water quality refers to the chemical , physical , and biological characteristics of water based on the standards of its usage. The most common standards used to monitor and assess water quality convey the health of ecosystems , safety of human contact, and condition of drinking water. Water quality has a significant impact on water supply and oftentimes determines supply options. The parameters for water quality are determined by the intended use. Contaminants that may be in untreated water include microorganisms such as viruses , protozoa and bacteria ; inorganic contaminants such as salts and metals ; organic chemical contaminants from industrial processes and petroleum use; pesticides and herbicides ; and radioactive contaminants. Water quality depends on the local geology and ecosystem , as well as human uses such as sewage dispersion, industrial pollution, use of water bodies as a heat sink , and overuse which may lower the level of the water. The U.
The precursors of deterioration of immaculate Kashmir Himalaya water bodies are apparent. This study statistically analyzes the deteriorating water quality of the Sukhnag stream, one of the major inflow stream of Lake Wular. Statistical techniques, such as principal component analysis PCA , regression analysis, and cluster analysis, were applied to 26 water quality parameters. For nitrate, nitrite, and chloride, the trend in accumulation factor AF showed that the downstream concentrations were about 2. River water quality is of great environmental concern since it is one of the major available fresh water resources for human consumption [ 1 , 2 ]. Throughout the history of human civilization, rivers have always been heavily exposed to pollution, due to their easy accessibility to disposal of wastes. However, after the industrial revolution the carrying capacity of the rivers to process wastes reduced tremendously [ 3 , 4 ].
Management of our nation's water resources through planning and control of water pollution hinges on the availability and interpretation of water quality data on which to base management decisions. This paper is aimed at exploring graphical methods which allow rapid and informative analysis of water quality data. The graphical methods presented in this paper fall into two main categories. The first category relates to graphical procedures which are developed as part of a statistical test, such as discriminant analysis. The second category involves approximation of multivariate water quality data into two dimensions. The methods for accomplishing the latter are canonical decomposition and high-dimensional plotting.
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Nov 19, - Water Quality Data: Analysis and Interpretation by Arthur Hounslow on Our website is contains million ebook to download with easy trial.
Graphical interpretation of water quality data
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. A variety of methods are being used to display the information which is concealed in the quality variables observed in a water quality monitoring network.
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