Temporal variations analyses and predictive modeling of microbiological seawater quality

Lušić, Darija Vukić; Kranjčević, Lado; Mačešić, Senka; Jozić, Slaven; Linšak, Željko; Bilajac, Lovorka; Grbčić, Luka; Bilajac, Neiro

doi:10.1016/j.watres.2017.04.046

Cited by 26 publications

(14 citation statements)

References 25 publications

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“…Furthermore, in addition to attracting more people to the beach, warm and sunny periods undoubtedly resulted in a higher reduction of indicator bacteria counts in seawater. FIB concentration is greatly influenced by the weather and environmental conditions [ 32 , 39 , 40 ]. It is well-known that an unfavorable marine environment, particularly solar radiation, temperature, and salinity, have a negative effect on allochthones bacteria survival, reducing culturability of FIB in a very short period of exposure [ 41 , 42 , 43 , 44 ], considerably more intense than the culturability of S. aureus [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Staphylococcus aureus—An Additional Parameter of Bathing Water Quality for Crowded Urban Beaches

Topić¹,

Cenov²,

Jozić

et al. 2021

IJERPH

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During the last years, the report of the occurrence of waterborne disease symptoms related to non-enteric pathogens has increased, without any record of higher levels of indicator bacteria (Escherichia coli and intestinal enterococci). Therefore, the use of current indicators is not always adequate when assessing the overall potential health risk and the inclusion of additional parameters needs to be examined. This paper reports on the incidence and levels of Staphylococcus aureus at 258 locations in Primorje-Gorski Kotar County (Croatia) recorded by official bathing water quality monitoring, as well as supplemental monitoring carried out at the two most frequented beaches in the City of Rijeka. The number of bathers was found to be the main factor affecting S. aureus levels (r = 0.321, p < 0.05). The share of S. aureus positive samples from the official monitoring was significantly lower, when compared to the share of samples from supplemental monitoring (2.2% and 36.3%, respectively; p < 0.01). Besides the number of bathers, one of the main reasons is likely the higher sampling frequency. No correlation was found between S. aureus levels and the indicator bacteria. The results indicate that the determination of S. aureus and increased sampling frequency is recommended for overcrowded beaches.

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Section: Discussionmentioning

confidence: 99%

Staphylococcus aureus—An Additional Parameter of Bathing Water Quality for Crowded Urban Beaches

Topić¹,

Cenov²,

Jozić

et al. 2021

IJERPH

Self Cite

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“…Modeling provides flexible approaches to infer sources and processes associated with FIOs and other pathogens, overcoming some of the issues of the single indicator paradigm. Various statistical and machine learning models have been used to approach such problems of incorporating age of fecal pollution for source tracking or detection of viruses (Brion et al 2002;Black et al, 2007); identifying land use, environmental, and water quality parameters associated with FIOs and pathogens (Brion and Lingireddy, 1999;Viau et al, 2011;Wilkes et al, 2011;Gonzalez et al, 2012;Gonzalez and Noble, 2014;Hall et al, 2014;Herrig et al, 2015;Lušić et al, 2017); determining factors influencing particle attachment and virulence (Piorkowski et al, 2013); and optimizing microbial source tracking (Belanche-Muñoz and Blanch, 2008;Ballestè et al, 2010;Smith et al, 2010;Molina et al, 2014). Some other applications of modeling include using turbidity or rainfall to predict E. coli concentrations at unmonitored sites (Money et al 2009, Coulliete et al 2009, estimating E. coli loads using physical, chemical, and biological factors within a neural network (Dwivedi 2013), and hyporheic-groundwater interactions associated with transport of E. coli within sediments porewater (Dwivedi 2016).…”

Section: Manuscript To Be Reviewedmentioning

confidence: 99%

Peer Review #1 of "Maxent estimation of aquatic Escherichia coli stream impairment (v0.1)"

2018

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Background. The leading cause of surface water impairment in United States' rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment. Methods. We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model's ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters. Results. Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO 3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen. Discussion. Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural runoff , stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic the plethora of existing pathogens in water, incorporating modeling can fine tune an indicator's utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making.

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“…More recently, Lusic et al. (2017) reported significant diurnality in FIO concentrations at five Croatian bathing waters which were sampled at four hourly intervals between 02:00 and 20:00 local time, suggesting that the highest FIO concentrations occurred in the 06:00 samples, possibly driven by lower bactericidal solar irradiance in the preceding night-time period.…”

Section: Introductionmentioning

confidence: 98%

Within-day variability in microbial concentrations at a UK designated bathing water: Implications for regulatory monitoring and the application of predictive modelling based on historical compliance data

et al. 2018

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Prediction of bathing water quality is recommended by the World Health Organization (WHO), the European Union (EU) and the United States Environmental Protection Agency (USEPA) and is an established element in bathing water management designed to protect public health. Most commonly, historical regulatory compliance data are used for model calibration and provide the dependent variable for modelling. Independent (or predictor) variables (e.g. rainfall, river flow and received irradiance) measured over some antecedent period are used to deliver prediction of the faecal indicator concentration measured on the day of the regulatory sample collection. The implied linked assumptions of this approach are, therefore, that; (i) the independent variables accurately predict the bathing-day water quality; which is (ii) accurately characterized by the single regulatory sample. Assumption (ii) will not be the case where significant within-day variability in water quality is evident. This study built a detailed record of water quality change through 60 days at a UK coastal bathing water in 2011 using half-hourly samples each subjected to triplicate filtration designed to enhance enumeration precision. On average, the mean daily variation in FIO concentrations exceeded 1 log 10 order, with the largest daily variations exceeding 2 log 10 orders. Significant diurnality was observed at this bathing water, which would determine its EU Directive compliance category if the regulatory samples were collected at the same time each day. A sampling programme of this intensity has not been reported elsewhere to date and, if this pattern is proven to be characteristic of other bathing waters world-wide, it has significance for: (a) the design of regulatory sampling programmes; (b) the use of historical data to assess compliance, which often comprises a single sample taken at the compliance point on a regular, often weekly, basis; and (c) the use of regulatory compliance data to build predictive models of water quality.

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Temporal variations analyses and predictive modeling of microbiological seawater quality

Cited by 26 publications

References 25 publications

Staphylococcus aureus—An Additional Parameter of Bathing Water Quality for Crowded Urban Beaches

Staphylococcus aureus—An Additional Parameter of Bathing Water Quality for Crowded Urban Beaches

Peer Review #1 of "Maxent estimation of aquatic Escherichia coli stream impairment (v0.1)"

Within-day variability in microbial concentrations at a UK designated bathing water: Implications for regulatory monitoring and the application of predictive modelling based on historical compliance data

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