Can water composition and weather factors predict fecal indicator bacteria removal in retention ponds in variable weather conditions?

Valenca, Renan; Garcia, Lilly; Espinosa, Christina; Flor, Dilara; Mohanty, Sanjay K.

doi:10.1016/j.scitotenv.2022.156410

Cited by 8 publications

(4 citation statements)

References 138 publications

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“…Moreover, random forest, support vector machines and classification/regression trees, as implemented in our analysis, were also the leading algorithms in complex environmental studies. These applications examined the infiltration rates of permeable stormwater channels, faecal indicator prediction in bathing waters, faecal contamination in environmental samples, conventional water quality indices, water quality in constructed wetlands, water quality index, level of algal bloom in reservoirs and seasonal variability of E. coli in irrigation ponds [ 66 ].…”

Section: Resultsmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

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Section: Resultsmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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“…Of common importance across the correlation and cTREE analysis were the concentrations of NO 3 and FDOM (Figure 4). Dissolved organic and inorganic nutrients have commonly shown positive associations with fecal bacteria concentrations in ponds [43,[46][47][48] and lakes [49][50][51]. Two possible reasons for this include the simple availability of nutrients used for growth and persistence [52][53][54] and the cooccurrence of nutrients with precipitation events which can transport nutrients to waterways along with fecal bacteria [51,55,56].…”

Section: Discussionmentioning

confidence: 99%

Spatial Variation of Tetracycline-Resistant E. coli and Relationships with Water Quality Variables in Irrigation Water: A Pilot Study

Stocker

Smith

Pachepsky

2023

Applied Microbiology

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Irrigation waters may facilitate the spread of antibiotic-resistant bacteria or genes to humans and animals. Monitoring of resistance in irrigated waters has become common; however, many studies do not incorporate a spatial component into sampling designs. The objective of this work was to assess spatiotemporal variations in tetracycline-resistant E. coli in an irrigation pond. Water samples were collected at 10 locations and two different water depths, and in situ and laboratory water quality measurements were performed. The percentage of E. coli resistant to the low (4 μg mL−1) and high (16 μg mL−1) tetracycline doses varied by date and location but were observed to be as high as 12.7% and 6.3% of the total population throughout the study, respectively. While significant differences were not observed between resistance levels measured at different depths, on one date resistant E. coli were only detected in samples collected at depth. Nitrate, fluorescent dissolved organic matter, and dissolved oxygen concentrations were found to be the leading control variables for the percentage of resistant E. coli. This work demonstrates that there may be substantial spatial variability in concentrations of antibiotic-resistant E. coli in irrigation ponds which should be accounted for in the design of monitoring programs.

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“…These methods are preferred because of easiness in operation, highly consistent and low cost (Tiwari et 91 al., 2016(Tiwari et 91 al., , 2018Zhang et al, 2022) [7,8,9]. By applying these protocols, viable and culturable cells of FIB can be counted in order to correlate them with health risks factors (Bisimwa et al, 2022;Valenca et al, 2022) [10,11]. Cultural methods need time for 94 sampling analysis and require at least 24-48 hours to export results because of the necessity for bacterial growth (Foddai & Grant, 2020) [12].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

JBRS

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This study was conducted to introduce and assess a novel automated system for monitoring of Fecal Indicator Bacteria and therefore evaluate seawater quality. The system presents the comparative advantage that it can give accurate results in real time and in the field. Here, we develop the seawater analyzer and evaluate the process by comparing the function of the device with plate count technique known as gold standard method. We demonstrate a system that changes color with the bacterial presence and is considered to be accurate, rapid and precise to warn for fecal contamination. We also present results by applying the method in different seacoasts regarding the swimming activity during the winter months but also the activity that concerns only at the beginning of the summer season June to September. A chi-square test of independence was performed between ISO method for E. coli and the bioanalyzer and between ISO method for E. faecalis and the bioanalyzer, p = 0.488 and p = 0.269, respectively. The novelty of the proposed method is that it integrates automated boiling as an extraction method to concentrate bacteria onto a filter, and it uses LAMP PCR to produce a portable optical sensor for environmental pathogen monitoring. This method is considered to be appropriate for bacterial detection in seawater, capable of monitoring coastal sites with results comparative to the gold standard method.

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Can water composition and weather factors predict fecal indicator bacteria removal in retention ponds in variable weather conditions?

Cited by 8 publications

References 138 publications

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Spatial Variation of Tetracycline-Resistant E. coli and Relationships with Water Quality Variables in Irrigation Water: A Pilot Study

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