Biofilms Impact on Drinking Water Quality

Farkas, Anca; Ciatarâş, Dorin; Bocoş, B.

doi:10.5772/32808

Cited by 6 publications

(4 citation statements)

References 71 publications

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“…by living ecologically in drinkingwater-associated biofilm. 30,31 Bacterial identification Based on morphological, biochemical, and genomic identification, only 7 bacterial strains were found to be prevalent in 6 different microenvironments. This group of bacteria belongs to γ-proteobacteria, namely, A dhakensis, P putida, K pneumoniae, E coli, S typhimurium, and L nimipressuralis.…”

Section: Seasonal Variation In Bacterial Population and Diversitymentioning

confidence: 99%

Effect of Seasonal Variation on Bacterial Inhabitants and Diversity in Drinking Water of an Office Building, Delhi

Yadav

Singh

Goyal

2019

Air, Soil and Water Research

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The work reported in this article raises some serious concern about the drinking water quality and its standards. Mere presence or absence of an indicator organism does not assure that the water is safe for drinking purposes. Instead of infecting directly, many pathogens pass through a host and retrieve their virulent properties by causing diseases/infections in humans. Pathogenic bacteria which exist in aquatic habitats show a unique and peculiar pattern of appearing or reappearing in different microenvironments. Several factors that prevail in the water system make a safe house for the growth, proliferation, and colonization of microorganisms. In our case, 6 different microenvironments inside the premises of an office building were taken as the sampling sites to study the effect of seasonal variations (summer, monsoon, and post-monsoon/winter) on bacterial diversity and inhabitants. Results suggested that the presence of total and thermotolerant coliforms were highest in the monsoon followed by summer and post-monsoon/winter seasons. To know the bacterial diversity and pattern of appearance/reappearance prevailing in the water system, bacterial strains were analyzed by 16S rRNA sequencing which showed Pseudomonas putida to be the predominant identified bacterial strain occurring about 38% to 77% in all 3 seasons. This was followed by Lelliottia nimipressuralis (6%-21%), Escherichia coli (4%-18%), Salmonella typhimurium and Aeromonas dhakensis (4%-10% each), and Klebsiella pneumoniae (5%-6%). Despite the absence of other opportunistic bacteria, P putida was reported to be present as a single organism in water coolers and dispensers. This might be due to the persistent nature of P putida in low-nutrient environments and capable of colonizing by forming a rigid biofilm inside the water cooler/dispenser which makes a conducive environment for it.

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Section: Seasonal Variation In Bacterial Population and Diversitymentioning

confidence: 99%

Effect of Seasonal Variation on Bacterial Inhabitants and Diversity in Drinking Water of an Office Building, Delhi

Yadav

Singh

Goyal

2019

Air, Soil and Water Research

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“…The most noticeable negative mean rank for TCC was observed in the winter (-mean rank 53.2) for 8.3 percent of the farms, indicating a medium effect size (d = 0.53). These notable differences in the numbers of microorganisms in the water between the WS and WTs could be caused by the presence of filter stations or disinfection systems within the distribution system [23,24].…”

Section: Discussionmentioning

confidence: 99%

Seasonal Variations Alter Drinking Water Quality at Different Points in The water Distribution Systems of Cattle Farms in Egypt

Kamal,

Khalf,

Mohamed

et al. 2025

Egyptian Journal of Veterinary Sciences

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HE monitoring of seasonal variations in drinking water quality (DWQ) within the drinking water distribution system (DWDS) of Egyptian dairy and beef farms affected by emerging epidemics is the main focus of this work. Across 132 farms, the DWQ was examined at four locations along the DWDS: the water source (WS), water tank (WT), calf trough (CT), and adult trough (AT). Correlations between DWQ and DWDS sample points, DWQ and microbial composition, and water temperature (Tw) and ambient temperature (Ta) were found by statistical analysis. Seasonal variations were highlighted by the strong effect sizes (d=0.88-1) that showed significant differences between Tw and Ta. Significant differences with medium to strong effect sizes (d= 0.53-0.85) in physicochemical DWQs were noted between WSs and house troughs (HTs). Furthermore, significant differences in the small to large effect sizes (d = 0.47 -0.87) of microbial DWQ between winter and summer were found. Subsequent investigations revealed significant differences, with variable effect sizes, in the microbial DWQs between the WS and WT points (d = 0.41 -0.65), between the WT and CT points (d = 0.51 -0.57), and between the WT and AT points (d = 0.56 -0.65) in the DWDS (ranging from small to large). The investigation concluded that variations in microbial DWQ were caused by interactions between seasonal variations in Ta and Tw, which in turn affected the DWDS. Furthermore, the DWDS had a major effect on the physicochemical characteristics of the DWQ that were observed on the cattle farms.

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“…The genus Pseudomonas is of particular concern because of the diversity of obligate and opportunistic pathogens contained within the genus that caused nosocomial infections in susceptible patients and their high intrinsic resistance to a variety of antibiotics, including βlactams, aminoglycosides and fluroquinolones that made them very difficult to eliminate. The Bacillus produced putative virulent factors capable for triggering infections, responsible for endocarditis and neurological cases (Farcas, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…More so, the use of bacterial inactivation (chlorination and chemotherapeutic agents) seldom permits the growth of some potentially pathogenic species not observed by total coliform and HPC tests, such as Pseudomonas, Bacillus and Mycobacterium. Consequently, improvements in culture-based independent sequencing techniques now allows the evaluation of microbial consortia in much greater extent than was earlier performed from culture-based dependent approach, such as practiced in HPC (Farcas, 2012). The previous understanding of bacterial identification relied on growth in enrichment culture should be accepted with caution.…”

Section: Introductionmentioning

confidence: 99%

ASSESSMENT OF 16S rRNA SEQUENCE AND HETEROTROPHIC PLATE COUNT (HPC) METHODS OF IDENTIFYING BACTERIA FROM DRINKING WATER SYSTEMS IN BENIN CITY METROPOLIS

Enaigbe¹,

Akpoka

Imade³

2019

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A variety of simple culture-based tests which are proposed to recover a wide range of microorganisms from water are collectively known as heterotrophic plate count (HPC) and used as an indirect indicator to give information about water quality. The aim of this study was to assess the heterotrophic plate count (HPC) culture-based dependent and 16S rRNA independent techniques of identifying bacteria. The HPC was conducted by incubating a filtered sample of water on R2A agar plates and enumerating the number of resultant bacterial colonies that grow on each plate. The molecular analysis was performed by extracting the deoxyribonucleic acid (DNA) from bacterial isolate and polymerase chain reaction (PCR) was done to obtain the amplicons (PCR products). Purified PCR products were sequenced by ABI V3.1 Big dye kit and the analysis of sequence was conducted by the basic local alignment search tool (BLAST) to identify their closest relatives. A total number of 17 isolates of Pseudomonas, Bacillus and Proteus were phenotypically identified, while the nucleotide sequence revealed the presence of 59 diverse strains with distribution (percentage) of Pseudomonas (25) 42.2 %, Bacillus 17 (28.8 %) and Proteus (17) 28.8 %. The investigated isolates when compared from gene data-base recorded genetic relatedness with similarity index of 61 to 100 %. The 16S rRNA gene sequences from DNA extractions showed microbial consortia in drinking water to comprise of a broad array of bacterial diversity, including those of critical concern to human health such as Pseudomonas and Bacillus. Finally, there was no significant correlation between the HPC test and 16S rRNA sequence analysis.

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Biofilms Impact on Drinking Water Quality

Cited by 6 publications

References 71 publications

Effect of Seasonal Variation on Bacterial Inhabitants and Diversity in Drinking Water of an Office Building, Delhi

Effect of Seasonal Variation on Bacterial Inhabitants and Diversity in Drinking Water of an Office Building, Delhi

Seasonal Variations Alter Drinking Water Quality at Different Points in The water Distribution Systems of Cattle Farms in Egypt

ASSESSMENT OF 16S rRNA SEQUENCE AND HETEROTROPHIC PLATE COUNT (HPC) METHODS OF IDENTIFYING BACTERIA FROM DRINKING WATER SYSTEMS IN BENIN CITY METROPOLIS

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