Dawn A. Shively, Meredith B. Nevers, Cathy Breitenbach, Mantha S. Phanikumar, Kasia Przybyla-Kelly, Ashley M. Spoljaric, Richard L. Whitman
Abstract
Predictive empirical modeling is used in many locations worldwide as a rapid, alternative recreational water quality management tool to eliminate delayed notifications associated with traditional fecal indicator bacteria (FIB) culturing (referred to as the persistence model, PM) and to prevent errors in releasing swimming advisories. The goal of this study was to develop a fully automated water quality management system for multiple beaches using predictive empirical models (EM) and state-of-the-art technology. Many recent EMs rely on samples or data collected manually, which adds to analysis time and increases the burden to the beach manager. In this study, data from water quality buoys and weather stations were transmitted through cellular telemetry to a web hosting service. An executable program simultaneously retrieved and aggregated data for regression equations and calculated EM results each morning at 9:30 AM; results were transferred through RSS feed to a website, mapped to each beach, and received by the lifeguards to be posted at the beach. Models were initially developed for five beaches, but by the third year, 21 beaches were managed using refined and validated modeling systems. The adjusted R(2) of the regressions relating Escherichia coli to hydrometeorological variables for the EMs were greater than those for the PMs, and ranged from 0.220 to 0.390 (2011) and 0.103 to 0.381 (2012). Validation results in 2013 revealed reduced predictive capabilities; however, three of the originally modeled beaches showed improvement in 2013 compared to 2012. The EMs generally showed higher accuracy and specificity than those of the PMs, and sensitivity was low for both approaches. In 2012 EM accuracy was 70-97%; specificity, 71-100%; and sensitivity, 0-64% and in 2013 accuracy was 68-97%; specificity, 73-100%; and sensitivity 0-36%. Factors that may have affected model capabilities include instrument malfunction, non-point source inputs, and sparse calibration data. The modeling system developed is the most extensive, fully-automated system for recreational water quality developed to date. Key insights for refining and improving large-scale empirical models for beach management have been developed through this multi-year effort.
This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 May 2016.
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#1,876,926
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#368
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#26,899
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Outputs of similar age from Journal of Environmental Management
#5
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Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,438 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 94% of its peers.
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We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.
