Titlebook: Statistical Methods for the Assessment of Point Source Pollution; Proceedings of a Wor D. T. Chapman,A. H. El-Shaarawi Conference proceedin

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Some Statistical Considerations in the Assessment of Compliance order to set limits or standards, the features of the discharge or the water body to be monitored must be characterized. This generally involves the fitting of a probability distribution to historical data or data from preliminary sampling and the choice of a statistic for the limit or standard. Mo

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A Proposed MISA Approach to Setting Limits and Assessing Compliancentaminants to surface waters. Based on 12-months of effluent monitoring data, effluent limit guidelines will be issued for 9 industrial and municipal sectors. The effluent limits will be based on the best available technology economically achievable and will involve extensive statistical analyses of

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Continuous Effluent Consents: Modelling and Compliance Testingharge standards and desired river quality class objectives are assessed within a probabilistic system of pollution control whereby a minimum level of 95 percent compliance with standards has to be achieved. The use of such a statistical framework permits occasional infringements of what would otherw

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Auditing the Quality of Effluent Dischargese achievement of good water quality is a matter of regulating the discharges from wastewater treatment plants. The effective audit of the quality of such discharges is a good basis for measuring progress in controlling pollution..To prove that control is effective, the techniques which underpin the

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The Effect of Autocorrelated Errors on Change-Detection Statisticsers of the regression coefficients and the ARMA processes. The problem of detecting changes in the regression parameters is considered. A change-detection statistic proposed by MacNeill (1978) for regression problems is modified for application to ARMA processes. The effect of autocorrelated errors

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State Estimation in Wastewater Engineering: Application to an Anaerobic Processrequires an adequate mechanistic dynamic model, and the identification and modelling of the major sources of stochastic disturbances in the process. The filter allows the on-line tracking of process variables which are not directly measurable. The use of an extended Kaiman filter is illustrated thro