Public Mis-Notification of Coastal Water Quality:  A Probabilistic Evaluation of Posting Errors at Huntington Beach, California

• Published in: Environmental science & technology Vol. 38; no. 9; pp. 2497 - 2504
• Main Authors: Kim, Joon Ha, Grant, Stanley B
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01.05.2004
• Subjects: Algorithms; Applied sciences; Beaches; California; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environment; Environmental Monitoring; Exact sciences and technology; Feces - microbiology; Forecasting; Humans; Information Services - standards; Marine; Models, Statistical; Natural water pollution; Pollution; Pollution, environment geology; Public Health; Reproducibility of Results; Risk Assessment; Seawaters, estuaries; Water Microbiology; Water Pollutants - analysis; Water pollution; Water quality; Water treatment and pollution; Pacific Ocean; California; United States; North America; East Pacific; America; Huntington Beach California; United States > US; INE, USA, California, Huntington Beach; USA, California; USA, California, Huntington Beach; Autocorrelation function; Statistical analysis; Probabilistic approach; Microbiology; Error rate; Coastal zone; Surveillance; Correlation analysis; Water quality; Health hazards; Public information; Water pollution; Feces; Sampling; Biological contamination; Bathing water; Seawater
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/es034382v

Whenever measurements of fecal pollution in coastal bathing waters reach levels that might pose a significant health risk, warning signs are posted on public beaches in California. Analysis of historical shoreline monitoring data from Huntington Beach, southern California, reveals that protocols used to decide whether to post a sign are prone to error. Errors in public notification (referred to here as posting errors) originate from the variable character of pollutant concentrations in the ocean, the relatively infrequent sampling schedule adopted by most monitoring programs (daily to weekly), and the intrinsic error associated with binary advisories in which the public is either warned or not. In this paper, we derive a probabilistic framework for estimating posting error rates, which at Huntington Beach range from 0 to 41%, and show that relatively high sample-to-sample correlations (>0.4) are required to significantly reduce binary advisory posting errors. Public mis-notification of coastal water quality can be reduced by utilizing probabilistic approaches for predicting current coastal water quality, and adopting analog, instead of binary, warning systems.