Title |
Comparative Evaluation of Statistical and Mechanistic Models of Escherichia coli at Beaches in Southern Lake Michigan
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Published in |
Environmental Science & Technology, February 2016
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DOI | 10.1021/acs.est.5b05378 |
Pubmed ID | |
Authors |
Ammar Safaie, Aaron Wendzel, Zhongfu Ge, Meredith B. Nevers, Richard L. Whitman, Steven R. Corsi, Mantha S. Phanikumar |
Abstract |
Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using distribution fitting to provide high-resolution time series data at the source, long-term "tracer" transport modeling based on observed electrical conductivity, better assimilation of meteorological data and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily-measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 2% |
Unknown | 55 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 20 | 36% |
Researcher | 7 | 13% |
Student > Master | 6 | 11% |
Student > Doctoral Student | 3 | 5% |
Other | 3 | 5% |
Other | 5 | 9% |
Unknown | 12 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Environmental Science | 17 | 30% |
Engineering | 9 | 16% |
Agricultural and Biological Sciences | 6 | 11% |
Earth and Planetary Sciences | 3 | 5% |
Medicine and Dentistry | 2 | 4% |
Other | 5 | 9% |
Unknown | 14 | 25% |