scholarly journals Waterborne illness and injury: a feasibility study of a site-specific predictive model for beach water quality at Beachway Park in the City of Burlington

2021 ◽  
Author(s):  
Ramien Sereshk
Keyword(s):  
Water Quality ◽  
Predictive Model ◽  
Real Time ◽  
Site Specific ◽  
E Coli ◽  
Time Basis ◽  
A Site ◽  
Persistence Model ◽  
Beach Water Quality ◽  
Beach Water

It is commonly assumed that the persistence model, using day-old monitoring results, will provide accurate estimates of real-time bacteriological concentrations in beach water. However, the persistence model frequently provides incorrect results. This study: 1. develops a site-specific predictive model, based on factors significantly influencing water quality at Beachway Park; 2. determines the feasibility of the site-specific predictive model for use in accurately predicting near real-time E. coli levels. A site-specific predictive model, developed for Beachway Park, was evaluated and the results were compared to the persistence model. This critical performance evaluation helped to identify the inherent inaccuracy of the persistence model for Beachway Park, which renders it an unacceptable approach for safeguarding public health from recreational water-borne illnesses. The persistence model, supplemented with a site-specific predictive model, is recommended as a feasible method to accurately predict bacterial levels in water on a near real-time basis.

scholarly journals Waterborne illness and injury: a feasibility study of a site-specific predictive model for beach water quality at Beachway Park in the City of Burlington

2021 ◽  
Author(s):  
Ramien Sereshk
Keyword(s):  
Water Quality ◽  
Predictive Model ◽  
Real Time ◽  
Site Specific ◽  
E Coli ◽  
Time Basis ◽  
A Site ◽  
Persistence Model ◽  
Beach Water Quality ◽  
Beach Water

It is commonly assumed that the persistence model, using day-old monitoring results, will provide accurate estimates of real-time bacteriological concentrations in beach water. However, the persistence model frequently provides incorrect results. This study: 1. develops a site-specific predictive model, based on factors significantly influencing water quality at Beachway Park; 2. determines the feasibility of the site-specific predictive model for use in accurately predicting near real-time E. coli levels. A site-specific predictive model, developed for Beachway Park, was evaluated and the results were compared to the persistence model. This critical performance evaluation helped to identify the inherent inaccuracy of the persistence model for Beachway Park, which renders it an unacceptable approach for safeguarding public health from recreational water-borne illnesses. The persistence model, supplemented with a site-specific predictive model, is recommended as a feasible method to accurately predict bacterial levels in water on a near real-time basis.

scholarly journals Predicting ‘very poor’ beach water quality gradings using classification tree

2015 ◽  
Vol 14 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Wai Thoe ◽  
King Wah Choi ◽  
Joseph Hun-wei Lee
Keyword(s):  
Water Quality ◽  
Hong Kong ◽  
Linear Models ◽  
Classification Tree ◽  
Classification Trees ◽  
Water Quality Prediction ◽  
E Coli ◽  
Poor Water Quality ◽  
Beach Water Quality ◽  
Beach Water

A beach water quality prediction system has been developed in Hong Kong using multiple linear regression (MLR) models. However, linear models are found to be weak at capturing the infrequent ‘very poor’ water quality occasions when Escherichia coli (E. coli) concentration exceeds 610 counts/100 mL. This study uses a classification tree to increase the accuracy in predicting the ‘very poor’ water quality events at three Hong Kong beaches affected either by non-point source or point source pollution. Binary-output classification trees (to predict whether E. coli concentration exceeds 610 counts/100 mL) are developed over the periods before and after the implementation of the Harbour Area Treatment Scheme, when systematic changes in water quality were observed. Results show that classification trees can capture more ‘very poor’ events in both periods when compared to the corresponding linear models, with an increase in correct positives by an average of 20%. Classification trees are also developed at two beaches to predict the four-category Beach Water Quality Indices. They perform worse than the binary tree and give excessive false alarms of ‘very poor’ events. Finally, a combined modelling approach using both MLR model and classification tree is proposed to enhance the beach water quality prediction system for Hong Kong.

scholarly journals Artificial neural network based nowcasting model for beach water quality

2021 ◽  
Author(s):  
Jainy Mavani
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Point Sources ◽  
Indicator Organisms ◽  
E Coli ◽  
Water Users ◽  
Ann Models ◽  
Artificial Neural ◽  
Persistence Model ◽  
Beach Water Quality

Recreational water users may be exposed to elevated pathogen levels that originate from various point and non-point sources. Current daily notifications practice depends on microbial analysis of indicator organisms such as Escherichia coli (E. coli) that require 18-24 hours to provide sufficient response. This research evaluated the use of Artificial Neural Networks (ANNs) for real time prediction of E. coli concentration in water at Toronto beaches (Ontario, Canada). The nowcasting models were developed in combination with readily available real-time environmental and hydro-meteorological data during the bathing season (June-August) of 2008 to 2012. The results of the developed ANN models were compared with historic data and found that the predictions of E. coli concentrations generated by ANN models slightly outperforms than currently used persistence model with better accuracy. The best performing ANN models for each beach are able to predict approximately 74% to 82% of the E. coli concentrations.

Epidemiological Study of Beach Water Pollution and Health-Related Bathing Water Standards in Hong Kong

1991 ◽  
Vol 23 (1-3) ◽  
pp. 243-252 ◽  
Author(s):  
W. H. S. Cheung ◽  
R. P. S. Hung ◽  
K. C. K. Chang ◽  
J. W. L. Kleevens
Keyword(s):  
Water Quality ◽  
Hong Kong ◽  
Epidemiological Study ◽  
Risk Levels ◽  
E Coli ◽  
Increased Risk ◽  
Health Related ◽  
Acceptability Criterion ◽  
Beach Water Quality ◽  
Beach Water

A prospective epidemiological study was undertaken in Hong Kong in 1987, in which 18,741 usable responses were obtained. It showed bathing in the coastal beaches of Hong Kong poses an increased risk of developing gastrointestinal, ear, eye, skin, respiratory and total illness. Swimmers immersed in the more polluted beach waters are exposed to a significantly higher risk of contracting swimming-associated gastrointestinal, skin, respiratory and total illness. E. coli was found to be the best indicator for swimming-associated gastroenteritis and skin symptoms amongst the bathers, and a linear relationship could be established. Staphylococci was a good indicator for ear, respiratory and total illness, and should be used in complementary to E. coli. Beach water quality objectives for both E. coli and staphylococci have been proposed. A 4-tier classification system (rather than a single acceptability criterion) based on swimming-associated health risks has been developed for the beaches of Hong Kong. Information on the bacterial water quality and health risk levels of individual beaches is reported to the public both annually and fortnightly, so that beach-goers can choose where to go for swimming based on health effects data.

scholarly journals Artificial neural network based nowcasting model for beach water quality

2021 ◽  
Author(s):  
Jainy Mavani
Keyword(s):  
Real Time ◽  
Meteorological Data ◽  
Point Sources ◽  
Indicator Organisms ◽  
E Coli ◽  
Water Users ◽  
Ann Models ◽  
Artificial Neural ◽  
Persistence Model ◽  
Beach Water Quality

Recreational water users may be exposed to elevated pathogen levels that originate from various point and non-point sources. Current daily notifications practice depends on microbial analysis of indicator organisms such as Escherichia coli (E. coli) that require 18-24 hours to provide sufficient response. This research evaluated the use of Artificial Neural Networks (ANNs) for real time prediction of E. coli concentration in water at Toronto beaches (Ontario, Canada). The nowcasting models were developed in combination with readily available real-time environmental and hydro-meteorological data during the bathing season (June-August) of 2008 to 2012. The results of the developed ANN models were compared with historic data and found that the predictions of E. coli concentrations generated by ANN models slightly outperforms than currently used persistence model with better accuracy. The best performing ANN models for each beach are able to predict approximately 74% to 82% of the E. coli concentrations.

scholarly journals Preliminary Microbiological Coastal Water Quality Determination along the Department of Atlántico (Colombia): Relationships with Beach Characteristics

2021 ◽  
Vol 9 (2) ◽  
pp. 122
Author(s):  
Zamira E. Soto-Varela ◽  
David Rosado-Porto ◽  
Hernando José Bolívar-Anillo ◽  
Camila Pichón González ◽  
Bertha Granados Pantoja ◽  
...  
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Urban Areas ◽  
Microbiological Quality ◽  
Salmonella Spp ◽  
E Coli ◽  
International Regulations ◽  
Rural And Remote Areas ◽  
Beach Water Quality ◽  
Beach Water

Beach water quality is an important factor concerning public health and tourism linked to the “Sun, Sea and Sand” market and is usually assessed in international regulations by the quantification of Escherichia coli and enterococci counts. Despite Salmonella spp. detection not being included in international normative, the presence/absence of this bacteria is also an indicator of seawater quality. The objective of this study was to determine microbiological quality of beach water at 14 beaches along the Department of Atlántico (Colombia) and its relationship with beach characteristics as beach typology (i.e., urban, village, rural and remote areas), presence of beach facilities (e.g., bars, restaurants, etc.) and streams outflowing into the coastline. Sampling program aimed to analyse E. coli and Salmonella spp., by culture-based and real time PCR methods, respectively. Microbiological outcomes were compared with beach characteristics, and a cluster analysis was performed. E. coli and Salmonella spp. were detected in 70% and 20% of samples, respectively. Highest E. coli counts were observed at beaches classified as urban and at Sabanilla, a rural beach with presence of numerous beach restaurants/bars. Salmonella spp. presence was associated with streams that lack wastewater treatment systems. Cluster analysis clearly evidenced the relationship between E. coli and Salmonella spp. and beach characteristics, allowing to obtain indications to implement management programs. According to data obtained, monitoring programs have to be especially carried out in urban areas and at places with beach facilities. This could enhance microbiological water quality and consequently, beachgoers safety and touristic beach attractiveness to international visitors.

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