Time Series Analysis of Water Pollution Data

Abstract

SUMMARY The techniques of time series analysis are presented in this paper and are applied to certain non-stationary water pollution data, in a meaningful way, to obtain a more effective analysis of such data. The analysis results in models which may be used for forecasting and the sample spectra which describe the underlying processes which generated the data. The use of the results can be of great value in the understanding and in management aspects of our environmental problems. It is becoming ever more apparent that there must be more effective analysis and interpretation of water pollution data in order to be able to attain water quality. The development of more sophisticated measuring devices and techniques which will continuously record several variables of interest (for example, dissolved oxygen (DO), biochemical oxygen demand (BOD), sunlight intensity, and temperature), requires the use of dataanalysis techniques to obtain a more fundamental understanding of the processes which generated these data. By applying the techniques of time series analysis, maximum results are possible. The ultimate objective of time series analysis is the estimation and reconstruction of the underlying generating process from a given time series. From this analysis possible laws may be discovered or indicated that the variables may obey, and thus allow future values of the variables to be forecasted. Hence time series analysis affords us a way to gain maximum results from the pollution variables and thus can aid in the understanding and management of water quality. In this paper we shall make use of the techniques of time series analysis to analyze certain dissolved oxygen water quality data. The dissolved oxygen concentration which exists in a stream while it is receiving an organic waste load is the variable by which the waste assimilation capacity of a stream is measured. The dissolved oxygen data used in this analysis were originally presented by Thomann [1967] and were continuously recorded by monitoring stations situated along the Delaware Estuary. Thomann used the technique of Fourier analysis to analyze these DO data, but this approach requires the assumption of fixed amplitudes, frequencies, and phases, and breaks