Scholarly record
MONITORING AND STATISTICS OF HEAVY METALS DAYLY DATA IN SURFACE WATER
Abstract
Specific statistical methods can be used when monitor the degree of water pollution in a target zone. The purpose of usage of time series properties is to understand the driving forces and structures that produce the observed data and to fit the data into a model and proceeds them to forecasting, monitoring or even feed back and feed forward control. The essential difference between modelling data via time series methods and using the process monitoring methods is that in time series analysis the data points taken over time may have internal structure such as autocorrelation, trend or seasonal variation. This paper uses some of the time series techniques for modelling and analysis of the daily heavy metal monitoring data measured in some important lakes of Bucharest during March – November 2007 – 2009. The daily registration of lead, mercury and cadmium concentrations in surfaces water form a univariate time series that consists in single scalar observations has been recorded sequentially over equal time increments. These time series display periodicity that represents sinusoidal fluctuations over entire week and seasonality over the year. The seasonal pattern was consistent with a wave sine model. The autocorrelation function was used to identify the autocorrelation structure of series.
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References5
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