Relations Between Environmental and Water-Quality Variables and Escherichia coli in the Cuyahoga River With Emphasis on Turbidity as a Predictor of Recreational Water Quality, Cuyahoga Valley National Park, Ohio, 2008

2009 ◽  
Author(s):  
Amie M.G. Brady ◽  
Meg B. Plona
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
National Park ◽  
Recreational Water ◽  
Recreational Water Quality ◽  
Water Quality Variables

scholarly journals Hydrodynamic modelling of recreational water quality using Escherichia coli as an indicator of microbial contamination

2018 ◽  
Vol 561 ◽  
pp. 179-186 ◽  
Author(s):  
Fasil Ejigu Eregno ◽  
Ingun Tryland ◽  
Torulv Tjomsland ◽  
Magdalena Kempa ◽  
Arve Heistad
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
Microbial Contamination ◽  
Recreational Water ◽  
Hydrodynamic Modelling ◽  
Recreational Water Quality

scholarly journals Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks

2018 ◽  
Vol 17 (1) ◽  
pp. 137-148
Author(s):  
Abdiel E. Laureano-Rosario ◽  
Andrew P. Duncan ◽  
Erin M. Symonds ◽  
Dragan A. Savic ◽  
Frank E. Muller-Karger
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Puerto Rico ◽  
Artificial Neural Networks ◽  
Dew Point ◽  
San Juan ◽  
Recreational Water ◽  
Recreational Water Quality ◽  
Artificial Neural ◽  
The U.S

Abstract Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

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