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SAMPLING FREQUENCY SELECTION FOR REGULATORY WATER QUALITY MONITORING 1
Author(s) -
Loftis J. C.,
Ward R. C.
Publication year - 1980
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1980.tb03904.x
Subject(s) - autoregressive model , statistics , sampling (signal processing) , confidence interval , range (aeronautics) , autocorrelation , selection (genetic algorithm) , series (stratigraphy) , mathematics , interval (graph theory) , sampling design , seasonality , sampling interval , sample size determination , sample (material) , correlation , quality (philosophy) , computer science , detector , population , geometry , philosophy , materials science , artificial intelligence , chemistry , combinatorics , sociology , composite material , biology , telecommunications , paleontology , epistemology , chromatography , demography
The selection of sampling frequencies in order to achieve reasonably small and uniform confidence interval widths about annual sample means or sample geometric means of water quality constituents is suggested as a rational approach to regulatory monitoring network design. Methods are presented for predicting confidence interval widths at specified sampling frequencies while considering both seasonal variation and serial correlation of the quality time series. Deterministic annual cycles are isolated and serial dependence structures of the autoregressive, moving average type are identified through time series analysis of historic water quality records. The methods are applied to records for five quality constituents from a nine‐station network in Illinois. Confidence interval widths about annual geometric means are computed over a range of sampling frequencies appropriate in regulatory monitoring. Results are compared with those obtained when a less rigorous approach, ignoring seasonal variation and serial correlation, is used. For a monthly sampling frequency the error created by ignoring both seasonal variation and serial correlation is approximately 8 percent. Finally, a simpler technique for evaluating serial correlation effects based on the assumption of AR(1) type dependence is examined. It is suggested that values of the parameter p 1 , in the AR(1) model should range from 0.75 to 0.90 for the constituents and region studied.