• 한국물환경학회지
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• 제26권1호
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• pp.111-115
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• 2010

Beaches in estuaries, bays, and harbors are frequently contaminated with indicators of human pathogens such as fecal indicator bacteria. Tracking down the sources of contamination at these enclosed beaches is complicated by the many point and non-point sources that could potentially degrade water quality along the shore. A mathematical framework was developed to test quantitative relationships between fecal indicator bacteria concentration in ankle depth water at enclosed beaches, the loading rate of fecal indicator bacteria from non-point sources located along the shore, physical characteristics of the beach that affect the transport of fecal indicator bacteria across the beach boundary layer, and a background concentration of fecal indicator bacteria attributable to point sources of fecal pollution that impact water quality over a large region of the embayment. Field measurements of fecal indicator bacteria concentrations and water turbulence at an enclosed beach were generally consistent with predictions and assumptions of the mathematical model, and demonstrated its utility for assessing waste load of non-point sources, such as runoff, bather shedding, bird droppings, and tidal washing of contaminated sediments.

• 한국해양바이오학회지
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• 제13권1호
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• pp.10-19
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• 2021

This study investigated how ozone treatment can successfully inactivate pathogenic bacteria in both artificial seawater and effluents discharged from the fishery quarantine station in Pyeongtaek Port, Korea. Vibrio sp. and Streptococcus sp. were initially inoculated into the artificial seawater. All microbes were almost completely inactivated within 10 min and 30 min by injecting 6.4 mg/min and 2.0 mg/min of ozone, respectively. It was discovered that the water storing Pleuronichthys, Pelteobagrus, and Cyprinus imported from China contained the indicator bacteria, Vibrio sp., Enterococcus sp., total coliforms, and heterotrophic microorganisms. Compared to the control, three indicator bacteria were detected at two to six times higher concentrations. The water samples displayed a diverse microbial community, comprising the following four phyla: Bacteroidetes, Proteobacteria, Firmicutes, and Actinobacteria. Almost all indicator bacteria were inactivated in 5 min at 2.0 mg/min of ozonation; comparatively, 92.9%-98.2% of the less heterotrophic microorganisms were deactivated within the same time period. By increasing the dosage to 6.4 mg/min, 100% deactivation was achieved after 10 min. Despite the almost complete inactivation of most indicator bacteria at high doses after 10 min, several bacterial strains belonging to the Proteobacteria have still been found to be resistant under the given operational conditions.

• 한국식품위생안전성학회지
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• 제14권1호
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• pp.1-8
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• 1999

There were few data for the distribution of the indicator organisms in the commercial plant foods, and for the normal flora and for the foodborne agents within the country. First of all it must be investigated the distribution of the indicator organisms. And also it is very important to prepare the sanitation criteria for the plant foods through the microbiological examination and the investigation of tendency to change of the indicator organisms according to the storage temperature and period. The average number of total viable counts for grains was 2.9$\times$105/g, psychrophilic bacteria 2.9$\times$105/g, heterotrophic bacteria 3.1$\times$105/g, heat-resistant bacteria 2.1$\times$103/g, Pseudomonas aeruginosa 23/g. That for beans was 6.3$\times$102/g, psychrophile 34/g, heterotroph 1.7$\times$102/g. That for sesames was 1.4$\times$105/g, coliform 350/g, psychrophile 7.4$\times$104/g, heterotroph 5.8$\times$104/g, Pseud. aeruginosa 2.3$\times$103/g. heat-resistant bacteria 150/g. That for potatoes was 2.0$\times$107/g, coliform 5.0$\times$104/g, psychrophile 1.8$\times$107, heterotroph 1.4$\times$107/g, heat-resistant bacteria 3.3$\times$104/, Staphylococcus 2.7$\times$105/g, fecal streptococcus 4.5$\times$103/g, Pseud. aeruginosa 7.0$\times$103/g. That for mushrooms was 1.2$\times$108/g, psychrophile 9.4$\times$107/g, heterotroph 1.0$\times$109/g, heat-resistant bacteria 1.6$\times$105/g, Pseud. aeruginosa 1.3$\times$103/g. That for vegetables was 5.9$\times$1011/g, coliform 1.8$\times$106g/, Staphylococcus 1.1$\times$1012/g, heterotroph 8.4$\times$1011/g, heat-resistant bacteria 7.6$\times$106/g, Staphylococcus 1.1$\times$107/g, fecal streptococcus 1.1$\times$104/g, Pseud. aerugniosa 5.2$\times$104/g. That for nuts 3.9$\times$104/g, coliform 3.9$\times$103/g, psychrophile 4.0$\times$104/g, heterotroph 3.2$\times$104/g, heat-resistant bacteria 400/g. In commercial grains and beans, SPC, psychrophile, heterotroph and heat-resistant bacteria stored at 1$0^{\circ}C$, 2$0^{\circ}C$, 3$0^{\circ}C$ were constant. Staphylococcus, coliform, Pseud. aeruginosa were decreased a little n grains, but were not detected in beans. In mushrooms, all indicator organisms were increased as time goes on and were increased rapidly at 2$0^{\circ}C$. In sesames, coliform was not detected at all temperature. psychrophile was increased for 7 days, the others were constant. In potatoes, SPC, psychrophile, heat-resistant bacteria, heterotroph had a tendency to increase and the others were constant. In vegetables, indicator organisms were had a tendency to increase, psychrophile, heterotroph were rapidly increased after 7 days. In nuts, SPC, coliform, psychrophile heterotroph, heat-resistant bacteria, Pseud. aeruginosa were constant, staphylococcus and fecal streptococcus were not detected.

• 한국수산과학회지
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• 제51권4호
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• pp.450-455
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• 2018

We examined correlations of the density of fish farms with the distributions of indicator bacteria (Escherichia coli, fecal streptococci) and a bacterial fish pathogen (Streptococcus parauberis) off the coastline of Jeju Island. Seawater samples were collected at four coastal sites on the Island [Aewol (control), Gujwa, Pyoseon and Daejeong] in June, August and October 2016. The indicator bacteria were generally more frequently isolated from samples taken in August when water temperatures and human activities on nearby beaches were highest. Although fish farms were least common at Daejeong, the numbers of isolated fecal indicator bacteria were highest in the seawater and effluent water collected from this site. Hence, fish farms were not likely major contributors of indicator bacteria at Daejeong. We found discrepancies between the isolated bacterial counts and the predicted bacterial copy numbers deduced from our qPCR results, indicating that this pathogen may exist in a viable but non-culturable (VBNC) state in seawater. Thus, livestock wastewater and chemical fertilizer loading off Jeju Island may negatively impact seawater quality more than the effluent released from fish farms does.

• 한국환경보건학회지
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• 제33권4호
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• pp.299-305
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• 2007

Distribution of fecal pollution indicator bacteria and environmental parameter were investigated of urban artificial lakes. An average concentration of temperature, pH, SS, DO, $COD_{Mn}$, T-P, T-N, Turbidity, Chl-a were $21.5^{\circ}C$, 8.07, 116.70 mg/l, 8.66 mg/l, 2.24 mg/1, 0.52 mg/l, 1.71mg/l, 80.54 NTU, and 52.12 mg/l respectively. From the results of bivariate correlation analysis, fecal contamination indicator bacteria were found to be mutually correlated. And turbidity and suspended solid were correlated. From the results of principal component analysis, four factors were extracted. And four factors of variance explained up to 81.5 percentage. Factor 1 was pollution pattern by fecal contamination, factor 2 was physical pollution pattern by pollution source, factor 3 was natural pollution by precipitation, and factor 4 was artificial pollution pattern by organism.

• 한국환경보건학회지
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• 제19권2호
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• pp.10-15
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• 1993

Hygienic condition of farm waters has an effect on the hygiene of dairy foods. So we examined Standard Plate Count, coliform, E. coli, heat-resistant bacteria, psychrophilic bacteria, Pseudomonas aeruginosa and enterococcus for the bacterial sanitation of 78 farm waters in Kyunggi Province. Of the 78 farm waters, the average number of psychrophilic bacteria was 750 $\pm$ 170/ml, SPC 440 $\pm$ 130/ml, Pseudomonas aeruginosa 130 $\pm$ 97/100/ml, Coliform 22 $\pm$ 17/ml, E. coli 10 $\pm$ 6/100/ml, Heatresistant bacteria 5 $\pm$ 1ml, and Enterococcus 2 $\pm$ 1/100/ml. The percent of over than 1000/ml in SPC and psychrophilic bacteria of 78 farm water was 11.5% and 23.1%, respectively. The rate of over than 10/ml in coliform and heat-resistant bacteria was 12.8% and 15.4%, respectively and the rate of over than 10/100 ml in E. coli, Pseudomonas aeruginosa and enterococcus was 8.9%, 33.3%, and 2.6%, respectively. Pseudomonas aeruginosa was no significant between any other indicator organisms, and psychrophilic bacteria was significant with only SPC but other indicator organisms were highly significant with each other.

• 한국수산과학회지
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• 제43권5호
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• pp.406-414
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• 2010

The impact of rainfall events on the sanitary indicator bacteria density of the shellfish-growing waters in Geoje Bay and Jaran Bay in Korea was investigated. The shellfish-growing area in Geoje Bay, which is a nearly closed basin, was not affected significantly, except near the stream mouth after 11.5 mm of rainfall in 1 day. However, most of the shellfish-growing water in the bay was polluted by fecal coliform bacteria after rain as heavy as 43.0 mm, and the levels of fecal indicator bacteria in some of the sea near the coast did not recover completely until 24 hours after the rainfall. By contrast, in Jaran Bay, which has no significant pollution source in the drainage area, although 9.3-490 MPN/100 mL of fecal coliform bacteria were detected near the stream mouth after rainfall of 33.5 and 81.0 mm, a very low level of the indicator bacteria was detected in the designated shellfish-growing area. During the investigation, the correlations between the sanitary indicator bacteria density and physical parameters, such as salinity and turbidity, were evaluated. Both the total coliform and fecal coliform densities were inversely correlated with salinity. Turbidity was positively correlated with the indicator bacteria density. The survey results suggest that for more efficient management of the shellfish-growing areas located in coastal areas, such as shellfish harvesting after rainfall, a detailed investigation of the effects of rainfall on the bacterial water quality in each growing area is needed.

• 한국환경보건학회지
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• 제37권4호
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• pp.289-297
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• 2011

Objectives: To estimate the microbial contaminant load discharged from livestock farms, we randomly selected livestock farmers of cattle, swine, and fowl and collected bacterial strains from domestic animals' feces and compost samples. Recently, as multi-antibiotic-resistant bacteria and super bacteria showing resistance to a variety of antibiotics have been reported one after another, the ecological and health hazard of antibiotic-resistant bacteria is emerging as an important issue. Methods: Monitored indicator microorganism constituents were totak coliform (TC), fecal coliform (FC), and aerobic bacteria. The multi-antibiotic-resistant bacteria were identified from investigated indicator microorganisms by 16S rRNA sequencing. Results: By microbiological analysis, the largest population of aerobic bacteria ($1.5{\times}10^5$ CFU/g) was found in cattle fecal compost, and total coliforms ($1.1{\times}10^7$ CFU/g) and fecal coliforms ($1.0{\times}10^5$ CFU/g) were found primarily in swine fecal compost, while the lowest population was found in fowl fecal compost. Among the 67 strains separated from aerobic bacteria, five strains expressing high antibiotic resistance were selected in each sample. We found the multi-antibiotic resistant strains to be Shigella boydii, Staphylococcus lentus, Acinetobacter sp. and Brevibacterium luteolum. Conclusions: These results suggest that increasing numbers of multi-antibiotic-resistant bacteria in the environment have a close relation to the reckless use of antibiotics with livestock.

• 한국수산과학회지
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• 제51권6호
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• pp.697-703
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• 2018

In 2017, total coliform (TC), fecal coliform (FC) and fecal Streptococci (FS) bacteria were examined in seawater samples collected at coastal sites on Jeju Island (Aewol as a control, Namwon, and Daejeong) and at Pohang (Yeongil as a control, Guryongpo-North, and Guryongpo-South) to examine the correlations between the density of fish farms and distributions of the indicator bacteria. Only a few TC, FC, and FS colonies were detected in all of the samples obtained from Jeju Island. Of note, 2,000 and 1,000 CFU of FS $100mL^{-1}$ were detected in samples from Guryongpo-South in June and August, respectively. Although the total area of approved fish farms located within 5 km of the sampling point at Guryongpo-South is 5-16 times smaller than in other regions, the number of indicator bacteria was highest in this region. Therefore, microbiological pollution in the Guryongpo-South region might be due to sources other than the effluent released from nearby fish farms.

• 한국환경농학회지
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• 제40권4호
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• pp.248-259
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• 2021

BACKGROUND: Contaminated water was a major source of food-borne pathogens in various recent fresh produce-related outbreaks. This study was conducted to investigate the microbial contamination level and correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water by logistic regression analysis. METHODS AND RESULTS: Agricultural water was collected from 457 sites including surface water (n=300 sites) and groundwater (n=157 sites) in South Korea from 2018 to 2020. Sanitary indicator bacteria (total coliform, fecal coliform, and Escherichia coli) and food-borne pathogens (pathogenic E. coli, E. coli O157:H7, Salmonella spp., and Listeria monocytogenes) were analyzed. In surface water, the coliform, fecal coliform, and E. coli were 3.27±0.89 log CFU/100 mL, 1.90±1.19 log CFU/100 mL, and 1.39±1.26 log CFU/100 mL, respectively. For groundwater, three kinds of sanitary indicators ranged in the level from 0.09 - 0.57 log CFU/100 mL. Pathogenic E. coli, Salmonella and Listeria monocytogenes were detected from 3%-site, 1.5%- site, and 0.6%-site water samples, respectively. According to the results of correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens by logistic regression analysis, the probability of pathogen detection increased individually by 1.45 and 1.34 times as each total coliform and E. coli concentration increased by 1 log CFU/100mL. The accuracy of the model was 70.4%, and sensitivity and specificity were 81.5% and 51.7%, respectively. CONCLUSION(S): The results indicate the need to manage the microbial risk of agricultural water to enhance the safety of fresh produce. In addition, logistic regression analysis is useful to analyze the correlation between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water.