Impact of Escherichia coli from stormwater drainage on recreational water quality: an integrated monitoring and modelling of urban catchment, pipes and lake

2020 ◽  
Vol 28 (2) ◽  
pp. 2245-2259 ◽  
Author(s):  
Yi Hong ◽  
Frédéric Soulignac ◽  
Adélaïde Roguet ◽  
Chenlu Li ◽  
Bruno J. Lemaire ◽  
...  
2018 ◽  
Vol 561 ◽  
pp. 179-186 ◽  
Author(s):  
Fasil Ejigu Eregno ◽  
Ingun Tryland ◽  
Torulv Tjomsland ◽  
Magdalena Kempa ◽  
Arve Heistad

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

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.


2004 ◽  
Vol 2 (2) ◽  
pp. 103-114 ◽  
Author(s):  
Julie Kinzelman ◽  
Sandra L. McLellan ◽  
Annette D. Daniels ◽  
Susan Cashin ◽  
Ajaib Singh ◽  
...  

Racine, Wisconsin, located on Lake Michigan, experiences frequent recreational water quality advisories in the absence of any identifiable point source of pollution. This research examines the environmental distribution of Escherichia coli in conjunction with the assessment of additional parameters (rainfall, turbidity, wave height, wind direction, wind speed and algal presence) in order to determine the most probable factors that influence E. coli levels in surface waters. Densities of E. coli were highest in core samples taken from foreshore sands, often exceeding an order of magnitude greater than those collected from submerged sands and water. Simple regression and multivariate analyses conducted on supplementary environmental data indicate that the previous day's E. coli concentration in conjunction with wave height is significantly predictive for present-time E. coli concentration. Genetic fingerprinting using repetitive element anchored PCR and cellular fatty acid analysis were employed to assess the presence of clonal isolates which indicate replication from a common parent cell. There were relatively few occurrences of clonal patterns in isolates collected from water, foreshore and submerged sands, suggesting that accumulation of E. coli, rather than environmental replication, was occurring in this system. Non-point source pollution, namely transport of accumulated E. coli from foreshore sands to surface waters via wave action, was found to be a major contributor to poor recreational water quality at the Lake Michigan beaches involved in this study.


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