Abstract
Climate change is expected to affect not only availability and quality of water, the valuable resource of human life on Earth, but also ultimately public health issue. A six-year monitoring (total 20 times) of Escherichia coli O157, Salmonella enterica, Legionella pneumophila, Shigella sonnei, Campylobacter jejuni, and Vibrio cholerae was conducted at five raw water sampling sites including two lakes, Hyundo region (Geum River) and two locations near Water Intake Plants of Han River (Guui region) and Nakdong River (Moolgeum region). A total 100 samples of 40 L water were tested. Most of the targeted bacteria were found in 77% of the samples and at least one of the target bacteria was detected (65%). Among all the detected bacteria, E. coli O157 were the most prevalent with a detection frequency of 22%, while S. sonnei was the least prevalent with a detection frequency of 2%. Nearly all the bacteria (except for S. sonnei) were present in samples from Lake Soyang, Lake Juam, and the Moolgeum region in Nakdong River, while C. jejuni was detected in those from the Guui region in Han River. During the six-year sampling period, individual targeted noxious bacteria in water samples exhibited seasonal patterns in their occurrence that were different from the indicator bacteria levels in the water samples. The fact that they were detected in the five Korea’s representative water environments make it necessary to establish the chemical and biological analysis for noxious bacteria and sophisticated management systems in response to climate change.
Keywords: Noxious bacteria, catchment-scale investigation, water resource, climate change
Introduction
Life on Earth are greatly affected by the dynamics of climate system, especially the Earth’s surface climate. In particular, infectious pathogens are emerging as a source of issue as many aspects of public health accompanying the climate change are widely recognized [1, 2]. The term pathogen covers a wide range of disease agents, such as virus, bacteria, parasitic germs, and fungi that can affect human beings either directly or indirectly through influencing the habitat, environment, or by competing with other pathogens. Climate change is a global phenomenon and is expected to accelerate in the future, especially in situations where the extent of climate change on Korean peninsula is relatively large (e.g., temperature rise, rainfall change, etc.) [3]. The annual mean temperature has been increasing at a rate of 0.52°C per decade and is significantly larger over urbanized areas [4], and it is anticipated that the incidence and geographic distribution of vector-borne diseases will change as a result [5].
Shigella is a genus of gram-negative pathogenic enterobacteria and a pathogenic variant of Eschericha coli comprising four groups, Shigella boydii, S. dysenteriae, S. sonnei, and S. flexneri [6]. Shigella species are waterborne and food-borne agents of bacillary gastrointestinal dysentery or shigellosis responsible for an estimated 80-165 million cases worldwide and account for a primary cause of childhood morbidity and mortality [7]. S. sonnei and S. flexneri result in most Shigellosis cases, with S. sonnei causing over 80% of all shigellosis infections and is increasingly found in developing countries [8]. In developing countries, especially where exist various public health problem caused by poor hygiene standards, a safe supply of drinking water influences the risk of public health.
Enterohemorrhagic E. coli O157 is a subtype of shiga toxin-producing E. coli and a primary food-borne pathogen causing the severe diseases in human such as hemolytic uremic syndrome, thrombotic thrombocytopenic purpura, and hemorrhagic colitis worldwide, although elderly and children are more expugnable [9].
Salmonellae are facultative anaerobic gram-negative bacteria belonging to the family Enterobacteriaceae and are a medically pivotal pathogen; two main species are Salmonella bongorin and S. enterica [10]. S. enterica has six subspecies that are composed of over 1500 subtypes some of which have profound medical significance [11]. Salmonella is an international food-borne intravacuolar pathogen causing a huge number of deaths and has a substantial cost burden. S. enterica subsp. enterica is responsible of more than 99% of human salmonellosis cases [12].
Legionella pneumophila, which causes community-acquired pneumonia that requires hospitalization, is an opportunistic pathogen that is omnipresent in aquatic environments in which it replicates in free-living amoebae [13, 14]. L. pneumophila pneumonia is strongly associated with high morbidity. Moreover, legionellosis is consistently reported as one of the top three most identified respiratory pathogens in community-acquired pneumonia, along with the hospital-acquired pneumonia [15].
Campylobacterota, formerly identified as Epsilon proteobacteria, are a whole bunch of gram-negative motile bacteria found in manifold ecological habitats [16]. Campylobacterota are pivotal clinical pathogens in human; the gut of half of the human beings is mass-dwelled with the stomach ulcer-causing bacterium Helicobacter pylori, while Campylobacter jejuni is a ubiquitous gastrointestinal pathogen and one of the majorly diagnosed bacterial food-borne pathogens in human and remains among the most common causes of bacterial gastroenteritis in many areas of world [17]. Campylobacteria infection causes the development of miscarriage, septicemia, gastroenteritis, proctitis, meningitis, and many neurological diseases, the foremost of which is Guillain-Barré syndrome among other central nervous system (CNS) diseases with similar acute progresses. Large global population (1-10% of the whole) can influence the risk of campylobacteriosis annually [18]. The non-food-borne transmission pathways for Campylobacter to human are birds and animals in which C. jejuni is part of normal flora, with the major pathway of transmission being the ingestion of contaminated food or drinking water [19].
Vibrio cholerae is a motile, aquatic curved-rod facultative gram-negative anaerobe belonging to the family of Vibrionaceae. Cholera caused by an etiological agent, V. cholerae, has been a serious epidemic secretary diarrheal disease that can quickly lead to severe dehydration and prove fatal within hours if untreated. Strains of V. cholerae inhabit both marine and freshwater ecosystems [20, 21]. Despite great betterments in hygiene, water quality, and sanitation, as well as in the clinical treatment, the disease is still estimated to cause about 100,000 deaths every year.
Climate change is causing water scarcity not only through increased temperatures and prolonged drought times but also through the degradation of water resources caused by increasing levels of pathogens and other contaminants posing significant health risks [22]. Thus, there is clearly a strong need for establishing management strategies and constant monitoring the water resources based on the results of testing water for contamination from relevant sources. Herein, we report the results on long-term (six-year) surveillance of noxious bacteria (E. coli O157, S. enterica, L. pneumophila, S. sonnei, C. jejuni, and V. cholerae) from August 2013 to February 2019 at various locations in Republic of Korea (Korea) to help the establishment of the management systems to maintain water quality and security.
Material and Methods
Collection Sites
A catchment scale investigation of the prevalence of E. coli O157, S. enterica, L. pneumophila, S. sonnei, C. jejuni, and V. cholerae was carried out. Water samples were collected 20 times from August 2013 to February 2019 from five surface water sampling locations: two lakes (Lake Soyang in Gangwon province and Lake Juam in Jeollanam province), Hyundo region (near Hyundo Bridge of Geum River at Shintanjin-dong in Daejeon Metropolitan City (Daejeon)), and two water intake plants (the Guui region on Han River in Seoul Special City (Seoul) and the Moolgeum region on Nakdong River in Gimhae-si) (Table 1). These five locations were selected to reflect the environment in response to changes in the landscape according to the climate change scenario by Intergovernmental Panel on Climate Change (IPCC): the Moolgeum region of Nakdong River, which is a subtropical zone; Lake Juam, which is classified as Representative Concentration Pathway (RCP) 4.5 proceeding to subtropical zone; the Guui region of Han River and the Hyundo region of Geum River, which are RCP 8.5 zones; and Lake Soyang, which is considered as non-subtropical zone in Korea.
Table 1.
Climate characteristics and geographic indexes the sample collection sites.
| Collection sites | Climate classification | Geographic indexes | Characteristics |
|---|---|---|---|
| Lake Soyang | Exceptional Subtropic Zone | 37.5654/127.4855 | Lake |
| The Guui region on River Han in Seoul | RCP 8.5 Subtropic Zone | 37.3305/127.0641 | River |
| The Hyundo region (near Hyundo Bridge of Geum River at Shintanjindong in Daejeon) | RCP 8.5 Subtropic Zone | 36.2724/127.2544 | River |
| Lake Juam | RCP 4.5 Subtropic Zone | 35.0340/127.1412 | Lake |
| The Moolgeum Region on the Nakdong River | Subtropic Zone | 35.1834/128.5837 | River |
Testing of Indicator Bacteria
Water samples were collected from each location. E. coli contamination were measured in samples using a Most Probable Number (MPN) assay. One hundred ml aliquots of water samples from each location were evaluated for total coliform (TC), fecal coliform (FC) and E. coli contamination and an IDEXX Colilert-18 and Quanti-Tray System (IDEXX Laboratories, USA). The collected water samples were placed immediately in a refrigerator (4°C) upon collection using sterile bags and transported to private laboratory in Kyonggi University for further processing. Briefly, the process started by adding a Colilert-18 reagent to each sample until it fully dissolved. The mixture was placed in a Quanti-Tray, which was sealed and incubated at 35°C and 44.5°C for 24 h each. Following incubation and positive well counts, the results were obtained using the IDEXX results table, where the number of colored and fluorescing large and small cells determined the MPN for coliform bacteria and E. coli. For each test sample, appropriate dilutions were prepared. This system is based on the MPN technique [23] and is a semiautomatic enzyme-based assay reduced to multi-wells. Control samples of commercially available sterile water were included along with the samples to evaluate cross- contamination.
Quality of the Collected Water Samples
Physiochemical parameters such as pH, total dissolved solids, dissolved oxygen (DO), total nitrogen, ammonia, nitrate, nitrite, phosphate, and sulfate were analyzed according to the Standard Methods for the Examination of Water and Wastewater [24]. Turbidity and conductivity were measured with a HACH 1900C portable turbidity meter (HACH, USA) and a HACH sension 5 conductivity meter (HACH), respectively. pH was measured on-site using individually calibrated portable testers. Chemical oxygen demand (COD) and ammonium content (NH3-N) were measured according to standard method [25, 26].
Sample Collection and Analysis
The targeted bacteria were S. sonnei, E. coli O157, S. enterica spp., L. pneumophila, C. jejuni, and V. cholerae. Spatially distributed samples were aseptically collected at five locations using sterile containers (Table 1). Samples were simultaneously and in parallel examined for the detection of 6 noxious bacteria. One-liter aliquots from each of five consecutive sampling were filtered using 0.2 mm filters to collect particulates. The filters were processed and extracted DNA using GeneAll Exgene Soil DNA kits (GeneAll Biotechnology, Korea) according to the manufacturer’s recommendation. The concentration of the extracted DNA was determined by measuring ultraviolet absorbance at 260 nm using a spectrophotometer (NanoDrop ND-1000, Thermo Fisher Scientific, USA), after which the samples were stored at -70°C before use.
Real-time PCR analysis was conducted with 10 ul of SYBR green master mix (Thermo Fisher Scientific) and 10 pM specific primer sets in a reaction volume of 20 ul using CFX96 Real-time PCR system (Bio-Rad Laboratories, USA). The primer set sequences and reaction conditions for each targeted noxious bacterium and the amplified target sizes represent in Table 2. The specificity of the primers was confirmed using a BLAST search in GenBank database from NCBI. For each bacterium tested in this monitoring, the BLASTn searches yield no solid match to any of the other identified bacterium reference sequences. Specificity tests were performed using conventional PCR techniques for each species or subspecies primer set against DNA samples from various bacterium strains [27]. Matches between the cyclic quantification (Cq) value and each of noxious bacteria detection was verified, and positive estimation was determined for a single peak using the Cq value. The positive samples were analyzed and confirmed by sequencing the 16s rDNA fragments by Macrogen Inc. (Korea). Analysis of the derived nucleotide sequences was performed for matching genotypes using the NCBI-BLAST service to target the noxious bacteria.
Table 2.
Primer set sequences used for noxious bacteria and cycling parameters in this study.
| Species | Target gene | Primer sequences | Product size | Cycling parameters |
|---|---|---|---|---|
| Shigella sonnei | Hypothetical protein | F: 5’-ACGCGTTAAAGATGATGCCTGTT-3’ | 325 bp | Initial denaturation: 95°C (2.5 min) 45 cycles of 95°C (10 sec), 60°C (20 sec) Denaturation: 95°C (10 sec) Slope range: 65-95°C for melting curve and melting peak |
| R: 5’-TGCCGCTAAAATCCTTCTGTCCT-3’ | ||||
| E. coli O157 | Hypothetical protein | F: 5-GCCGTACATGCTGCTGAGAGTC-3’ | 215 bp | Initial denaturation: 95°C (2.5 min) 45 cycles of 95°C (10 sec), 59°C (20 sec) Denaturation: 95°C (10 sec) Slope range: 65-95 °C for melting curve and melting peak |
| R: 5’-TAGCCCCATATAGCGTAAGAAT-3’ | ||||
| Salmonella enterica | Hypothetical protein | F: 5’-CGCGTCGCTTCGTTCTGTATCAT-3’ | 353 bp | Initial denaturation: 95°C (2.5 min) 45 cycles of 95°C (10 sec), 50°C (20 sec) Denaturation: 95°C (10 sec) Slope range: 65-95 °C for melting curve and melting peak |
| R: 5’-GCGCTGCCACTCTCGGTTTCTTAT-3’ | ||||
| Legionella pneumophila | Hypothetical protein | F: 5’-ACACGTTGAAGAGGAGTTAG-3’ | 264 bp | Initial denaturation: 95°C (2.5 min) 45 cycles of 95°C (10 sec), 59°C (20 sec) Denaturation: 95°C (10 sec) Slope range: 65-95 °C for melting curve and melting peak |
| R: 5’-ACAAGCTCTACTTCAATGCC-3’ | ||||
| Vibrio cholerae | Hypothetical protein | F: 5’-CCGTTGAGGCGAGTTTGGTGAGA-3’ | 195 bp | Initial denaturation: 95°C (2.5 min) 45 cycles of 95°C (10 sec), 52°C (20 sec) Denaturation: 95°C (10 sec) Slope range: 65-95 °C for melting curve and melting peak |
| R: 5’-GTGCGCGGGTGGAAACTTATGAT-3’ | ||||
| Campylobacter jejuni | Hypothetical protein | F: 5’-AAAAAGAGATTTATATTAACAAAA-3’ | 177 bp | Initial denaturation: 95°C (2.5 min) 45 cycles of 95°C (10 sec), 55°C (20 sec) Denaturation: 95°C (10 sec) Slope range: 65-95 °C for melting curve and melting peak |
| R: 5’-GCTTAATTGTATAGTTTATATTATC-3’ |
Results
Physicochemical Parameters in the Water Samples
There were slight variations in physiochemical parameters among the water sample collecting sites. The water temperature at the Guui region and Lake Juam tended to increase slightly, while no definite trend was observed for precipitation (Data not shown). The other collection sites did not show a definite tendency in the parameters of precipitation or water temperature. Table 3 summarizes the physiochemical parameters of water samples from the five sample collecting sites during the periods from August 2013 to February 2019. All the parameters other than precipitation and water temperature fluctuated continuously through the year. The pH values are in the range of 6.5 to 8.5 (in a descending order: Guui region > Moolgeum region > Hyundo region > Lake Soyang > Lake Juam), which according to the World Health Organization (WHO) guidelines for drinking water [28], the pH values at the surface fall within the normal limit. Most of the monitoring locations had a sufficient DO level (more than 7 mg/l), although one was borderline (Lake Juam). The conductivity values of the collected water samples were ranged from 70 to 350 mS/cm on average, which are well within the unpolluted freshwater range of 10 to 1,000 mS/cm. The average amount of total nitrogen in the water samples from the collection sites was 1.898 ± 0.850 mg/l, while ammoniacal nitrogen (NH3 or NH4+) did not consistently exceed 0.3 mg/l. The acceptable amount of nitrates in drinking water is up to around 44 mg/l [29], so the samples from the locations were well within this (0.4-2.1 mg/l). The phosphate level in the samples was 0.025 ± 0.021 mg/l, which is well within the WHO guideline of 1 mg/l. In general, the Moolgeum region on Nakdong River had the highest values for BOD, COD, conductivity, total nitrogen, total phosphorus, and phosphate, while the Guui region on Han River in Seoul had highest values for pH, DO, ammonia, and nitrate.
Table 3.
Physicochemical characteristics of the water samples of the five collected sites in this study.
| Parameter Sites | Temp. (°C) | pH | Dissolved oxygen (mg/l) | BOD (mg/l) | COD (mg/l) | Conductivity (mS/cm at 20°C) | Total nitrogen (mg/l) | Ammonia (mg/l) | Nitrate (mg/l) | Total phosphorus (mg/l) | Phosphate (mg/l) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| The Guui region on the Han River in Seoul | 12.71±9.11 | 8.125±0.17 | 12.24±1.89 | 1.515±0.51 | 4.115±0.61 | 209.3±47.96 | 2.684±0.47 | 0.097±0.091 | 2.028±0.33 | 0.038±0.019 | 0.0065±0.008 |
| The Moolgeum region on the Kakdong River | 15.06±9.50 | 8.075±0.37 | 10.925±2.45 | 1.955±0.52 | 6.265±1.22 | 336.25±104.24 | 2.742±0.57 | 0.086±0.035 | 1.959±0.60 | 0.047±0.027 | 0.0112±0.013 |
| The Hyundo region (near Hyundo Bridge of Geum River in Daejeon) | 13.26±6.85 | 7.905±0.23 | 10.335±2.50 | 0.73±0.25 | 3.875±0.61 | 167.35±26.31 | 1.479±0.25 | 0.106±0.273 | 1.088±0.34 | 0.015±0.00 | 0.0032±0.005 |
| Lake Soyang | 9.45±4.70 | 7.33±0.35 | 8.395±1.65 | 1.18±0.26 | 2.09±0.32 | 76.2±5.73 | 1.867±0.31 | 0.024±0.014 | 1.377±0.17 | 0.011±0.006 | 0.0025±0.002 |
| Lake Juam | 11.93±5.40 | 6.82±0.37 | 6.985±3.14 | 0.82±0.19 | 2.94±0.47 | 79.6±8.08 | 0.718±0.11 | 0.069±0.062 | 0.472±0.12 | 0.132±0.005 | 0.0033±0.003 |
Indicator Bacteria and Water-Quality Monitoring Stations
The monitoring points in this study were selected for the water quality measurements due to the needs for long-term monitoring and management of Korean rivers and lakes and the links between the nearby water quality measuring network points according to the prediction scenario for climate change: Lake Soyang in the exceptional subtropical zone and the Guui region on the Han River in Seoul, Hyundo region (near Hyundo Bridge of Geum River at Shintanjin-dong in Daejeon), Lake Juam, and the Moolgeum region on Nakdong River in the subtropical zone.
In practice, it is impossible to enumerate all pathogens in water-source because of the absence of specific detection techniques. Thus, indicator bacteria including TC, FC and E. coli are traditionally used to indicate the presence of a pathogens, especially in wastewater as well as other intestinal pathogens [30]. The presence of TC and FC is indicative of human fecal contamination. TC, FC, and/or E. coli were detected in almost samples collected across 6 year monitoring and 23% of the samples exceeded the regulations provided by the Pennsylvania Department of Environmental Protection (PA DEP) form TC (5,000 CFU/100 ml) at concentration ranging from 0 to 1.9 × 109 MPN/100 ml (CFU and MPU are equivalent), which were mainly observed between August and October [31]. Concentrations of the FC in 13% exceeded the PA DEP regulations for fecal coliforms (200 CFU/ 100 ml) during the investigation period [31]. Spatially, the Guui region of Han River was the highest contaminated place among the monitored sites in this study, followed by Moolgeum region of Nakdong River. During the period under investigation, the TC (average: 1.2 × 104 MPN/100 ml) rather than FC (average: 563 MPN/100 ml) or E. coli (average: 313 MPN/100 ml) were detected highest. The averages of three indicator bacteria in August 2017 were highest (3.8 × 104, 1.0 × 104, and 5.8 × 103 MPN/100 ml for TC, FC and E. coli, respectively) (Table 4). Lake Soyang and Lake Juam had lower contamination than the other sites in this study. However, it is not evident from the information on precipitation and physicochemical characteristics whether the collected indicator bacteria in the collected samples were higher in August 2017 compared to other collection periods. Overall, the distribution of the indicator bacteria was found to be the highest in August (when the water temperature was high), followed by October.
Table 4.
Total indicator bacteria (MPN/100 ml) including TC, FC, and E. coli during the monitoring period.
| Indicator bacteria | Collection sites | Aug, 2013 | Oct, 2013 | Dec 2013 | Feb, 2014 | Aug, 2014 | Oct, 2014 | Dec, 2014 | Feb, 2015 | Dec, 2015 | Feb, 2016 | Apr, 2016 | Jun, 2016 | Aug, 2017 | Oct, 2017 | Dec, 2017 | Feb, 2018 | Aug, 2018 | Oct, 2018 | Dec, 2018 | Feb, 2019 | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total Coliform (TC) | Lake Soyang | 90,000 | 12,000 | 5,400 | 100 | 610 | 2,400 | 550 | 67 | 32 | 70 | 150 | 1,270 | 3,700 | 1,300 | 160 | 7 | 0 | 3,400 | 170 | 65 | 6,072.55 |
| Han River | 190,0000 | 85,000 | 5,500 | 200 | 5,200 | 1,200 | 370 | 130 | 1,700 | 1,400 | 480 | 1,100 | 160,00 | 1,700 | 2,900 | 4,400 | 14,000 | 1,200 | 610 | 300 | 23,869.5 | |
| Geum River | 63,000 | 11,000 | 5,800 | 1,000 | 1,100 | 37 | 2,000 | 140 | 290 | 1,300 | 17,000 | 2,500 | 24,000 | 22,000 | 650 | 410 | 0 | 34,000 | 2,900 | 770 | 9,494.85 | |
| Lake Juam | 110,000 | 5,900 | 5,400 | 1,400 | 6,000 | 5,500 | 130 | 14 | 150 | 17 | 260 | 980 | 7 | 2,400 | 20 | 0 | 1,000 | 5,200 | 390 | 16 | 7,239.2 | |
| Nakdong River | 190,000 | 48,000 | 1,900 | 100 | 2,300 | 9,200 | 460 | 56 | 12 | 240 | 1,900 | 3,500 | 3,700 | 8,700 | 1,000 | 220 | 6,900 | 3,900 | 520 | 180 | 14,139.4 | |
| Fecal Coliform (FC) | Lake Soyang | 1 | 1 | 0 | 0 | 1 | 5.2 | 1 | 0 | 0 | 0 | 2 | 0 | 5 | 73 | 4 | 1 | 0 | 1 | 2 | 1 | 4.91 |
| Han River | 13 | 37 | 40 | 12 | 30 | 120 | 25 | 29 | 79 | 32 | 38 | 200 | 48,000 | 290 | 96 | 820 | 650 | 54 | 30 | 11 | 2530.3 | |
| Geum River | 51 | 2 | 5.1 | 119 | 0 | 6 | 0 | 3 | 3 | 670 | 31 | 290 | 2,100 | 96 | 22 | 9 | 0 | 150 | 120 | 1 | 183.905 | |
| Lake Juam | 1 | 5.2 | 3.1 | 0 | 0 | 310 | 2 | 0 | 0 | 0 | 8 | 0 | 0 | 68 | 2 | 0 | 200 | 3 | 0 | 0 | 30.115 | |
| Nakdong River | 4.1 | 1 | 1 | 0 | 5.2 | 310 | 6.3 | 17 | 1 | 0 | 9 | 6 | 32 | 610 | 37 | 4 | 220 | 35 | 3 | 2 | 65.18 | |
| E. coli | Lake Soyang | 1 | 1 | 0 | 0 | 0 | 4.1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.405 |
| Han River | 8.5 | 20 | 38 | 8.6 | 30 | 73 | 6.3 | 5 | 71 | 32 | 23 | 96 | 29,000 | 100 | 28 | 820 | 20 | 33 | 23 | 9 | 1,522.22 | |
| Geum River | 0 | 1 | 5.1 | 1 | 10 | 0 | 15 | 0 | 3 | 3 | 19 | 23 | 210 | 42 | 28 | 9 | 0 | 120 | 96 | 3 | 29.405 | |
| Lake Juam | 1 | 3 | 3.1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.61 | |
| Nakdong River | 2 | 1 | 1 | 0 | 4.1 | 240 | 0 | 3 | 0 | 0 | 6 | 5 | 11 | 5 | 0 | 1 | 0 | 14 | 0 | 1 | 14.705 |
Relationship between Physicochemical Parameters and Indicator Bacteria
Physicochemical parameters are among the major factors involved in the management and mitigation of non-point source pollution, and the effect of fecal contamination on the quality of water is a matter of quite concern. Information gained through the regular monitoring of water quality allows estimation of the likelihood pathogens-related waterborne disease [32]. In general, indicator bacteria (TC, FC, and E. coli) tended to decrease over time except in 2013 (Table 4). TC levels were higher in August and October of the year and remained at a certain level depending on the time of collection. However, TC levels in Lake Soyang and Lake Juam were the highest in October when the water temperature was low. The detection rates of FC and E. coli were stable throughout the monitoring periods. Finally, it is evident that the distribution of indicator bacteria was related to changes in turbidity due to water temperature and precipitation.
Detection of Noxious Bacteria in Monitoring Sites
Most of the targeted bacteria were found in 77% of the samples and at least one of the target bacteria was detected (65%) (Fig. 1, Table 5). Among all the detected bacteria, E. coli O157 were the most prevalent with a detection frequency of 22% (22/100), while S. sonnei was the least prevalent with a detection frequency of 2% (2/ 100 samples). Nearly all of the bacteria (except for S. sonnei) were present in samples from Lake Soyang, Lake Juam, and Nakdong River (Fig. 2), while C. jejuni was detected in those from Han River. During the six-year sampling period, individual targeted noxious bacteria in water samples exhibited seasonal patterns in their occurrence that were different from the indicator bacteria levels in the water samples. The occurrence of noxious bacteria in the samples was higher during the colder months (October, December, and February) than the warmer ones. However, after April 2016, the occurrences of noxious bacteria in the water samples dramatically decreased to 10.39%. This can be attributed to the authorities’ effort, such as sewage system management, to improve the water quality. Detection of TC, FC and E. coli in the water samples could not predict the total noxious bacteria presence.
Fig. 1.
Total positive incidence cases of noxious bacteria surveillance in this study during the monitoring period.
Table 5.
Positive incidence cases of each noxious bacterium at the five-water sample collecting sites during the monitoring period in this study.
| Aug. 2013 | Oct. 2013 | Dec. 2013 | Feb. 2014 | Aug. 2014 | Oct. 2014 | Dec. 2014 | Feb. 2015 | Dec. 2015 | Feb. 2016 | Apr. 2016 | Jun. 2016 | Aug. 2017 | Oct. 2017 | Dec. 2017 | Feb. 2018 | Aug. 2018 | Oct. 2018 | Dec. 2018 | Feb. 2019 | Total | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lake Soyang | S. sonnei | 0 | ||||||||||||||||||||
| E. coli O157 | Posi. | Posi. | Posi. | Posi. | Posi. | Posi. | 6 | |||||||||||||||
| S. enterica | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| L. pneumophila | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| V. cholerae | Posi. | Posi. | 2 | |||||||||||||||||||
| C. jejuni | Posi. | 1 | ||||||||||||||||||||
| Han River | S. sonnei | Posi. | 1 | |||||||||||||||||||
| E. coli O157 | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| S. enterica | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| L. pneumophila | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| V. cholerae | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| C. jejuni | 0 | |||||||||||||||||||||
| Geum River | S. sonnei | Posi. | 1 | |||||||||||||||||||
| E. coli O157 | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| S. enterica | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| L. pneumophila | Posi. | Posi. | 2 | |||||||||||||||||||
| V. cholerae | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| C. jejuni | Posi. | 1 | ||||||||||||||||||||
| Lake Juam | S. sonnei | 0 | ||||||||||||||||||||
| E. coli O157 | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| S. enterica | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| L. pneumophila | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| V. cholerae | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| C. jejuni | Posi. | 1 | ||||||||||||||||||||
| Nakdong River | S. sonnei | 0 | ||||||||||||||||||||
| E. coli O157 | Posi. | Posi. | Posi. | Posi. | Posi. | Posi. | 6 | |||||||||||||||
| S. enterica | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| L. pneumophila | Posi. | Posi. | Posi. | 3 | ||||||||||||||||||
| V. cholerae | Posi. | Posi. | Posi. | Posi. | 4 | |||||||||||||||||
| C. jejuni | Posi. | 1 | ||||||||||||||||||||
| Total | 6 | 20 | 13 | 10 | 1 | 1 | 0 | 4 | 7 | 7 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 77 |
Fig. 2.
Positive incidence cases of each noxious bacterium at each collecting site.
Statistical Analysis of Correlation between Indicator Bacteria and the Tested Noxious Bacteria
We performed correlation tests between the monitored six noxious bacteria and the tested three indicator bacteria (TC, FC, E. coli) using permutation technique [33]. Testing results are summarized in Table 6. Only between S. enterica and TC has p-value less than 0.05. All the other relationships were not able to look at a significant association. Even in the case of the association between S. enterica and TC, when their p-value were adjusted by the Bonferroni calibration, it did not produce any significant results. After Bonferroni adjustment, all the combinations show p-values larger than 0.05. Thus, we concluded that the current results show no statistically significant association in any combination.
Table 6.
A statistical association between the monitored noxious bacteria and the tested indicator bacteria.
| Noxious bacteria | Indicator bacteria | p-value | Bonferroni-adjusted p-value |
|---|---|---|---|
| S. sonnei | TC | 0.148 | 1 |
| FC | 0.304 | 1 | |
| E. coli | 0.38 | 1 | |
| E. coli 0157 | TC | 0.268 | 1 |
| FC | 0.065 | 1 | |
| E. coli | 0.905 | 1 | |
| S. enterica | TC | 0.014 | 0.252 |
| FC | 0.262 | 1 | |
| E. coli | 0.28 | 1 | |
| L. pneumophila | TC | 0.326 | 1 |
| FC | 0.806 | 1 | |
| E. coli | 0.327 | 1 | |
| V. cholerae | TC | 0.54 | 1 |
| FC | 0.102 | 1 | |
| E. coli | 0.97 | 1 | |
| C. jejuni | TC | 0.876 | 1 |
| FC | 0.204 | 1 | |
| E. coli | 0.264 | 1 |
Discussion
The worldwide burden of infectious waterborne disease is considerable, and the bacterial pathogens are strongly resistant in the water environment and to most disinfectants. Some bacterial agents such as S. sonnei, C. jejuni and E. coli O157 can contaminate pristine waters through wildlife and human activities. In addition, climate variables such as precipitation, temperature that have changed significantly as a result of global climate change are major driving forces of food- and waterborne diseases and alter the exposure pathways. These determinants could influence the fate and transport of pathogens, as well as their stability, reproduction rates, and viability in the environment. Therefore, sophisticated and consistent surveillance systems and means should be put in place to monitor the targeted pathogen candidates for serious waterborne diseases.
Some of the noxious bacteria exhibited spatial and seasonal patterns at the collecting sites in this study. The presence of C. jejuni in samples from four of the targeted sampling collection sites (except the Guui region of Han River) indicates that positive cases are in fall and winter (October, December and February) but not in spring and summer seasons (August, April, and June), which coincides with previously reported studies [34, 35]. In addition, the positive cases for L. pneumophila are in winter except for one in August 2013 at the Moolgeum region site. In the case of S. sonnei, there were only two positive cases of samples from the Guui region and the Hyundo region in October 2013 were not linked to seasonality. Similarly, the indicator bacteria TC, FC and E. coli were not consistently and significantly correlated with the detection of the targeted noxious bacteria (Table 6). These data indicate that indicator bacteria and physiochemical parameters used in this study are not potential candidates for predicting the presence of typical noxious bacteria such as S. sonnei, E. coli O157, S. enterica spp., L. pneumophila, C. jejuni, and V. cholerae in the surface water at the five targeted surface water sampling locations including the Guui region, the Moolgeum region, the Hyundo region, Lake Soyang and Lake Juam.
According to the results of the monitoring in this study, the occurrences of noxious bacteria in water samples were dramatically decreased after April 2016. Although it is difficult to elucidate the specific cause, this could be attributed to the authorities’ effort, such as sewerage system management and social good-informed cognition, to improve the water quality. Korea achieved 92.1% penetration rate of sewage into the advanced countries through the first National Sewage Comprehensive Plan (NSCP) (2007-2015) through continuous expansion of sewage treatment facilities and sewage systems, improved sewage maintenance, enhancement of sewerage and sewerage management, establishment of water resource circulation utilization systems, and improved sewage treatment technology and sewage sludge treatment [36].
This study has a critical limitation. First of all, in some years during the study, the collection of surface water samples has limitations that have not been carried out as originally planned and thus we were not able to proceed with consistent sample collection and monitoring during the summer season. In addition, since this study was only based on the genetic analysis using PCR methods, we were not able to determine the infectivity and pathogenesis despite the positive detection. Nevertheless, this study was designed and practiced at these specific sites as a project of the National Institute of Environmental Research funded by the Ministry of Environment of the Republic of Korea. In fact, despite the growing interest in monitoring noxious microorganisms, it is difficult to find a case of research on their distribution and monitoring related to climate change at Korea or abroad. The Ministry of Environment of the Republic of Korea recognized the need for this research to provide public health security and secure drinking water stability because of water temperature rise, flooding, drought and heat waves due to climate change increase the prevalence of noxious microbes. The Ministry of Environment had set a Priority Management List (PML) of 20 noxious microbes in groups including TC bacteria, FC bacteria, pathogenic E. coli, enterococci, fecal Streptococci, Pseudomonas as concerns about unregulated waterborne microbes increase.
In conclusion, it was not possible to determine the infectivity and pathogenicity on the six noxious bacteria examined in this study, and it was difficult to precisely identify any noticeable seasonal or regional effects. However, the fact that they were detected in the five Korea’s representative water environments comprising lakes, rivers, and drinking water collecting sites make it necessary to establish the chemical and biological analysis for noxious bacteria and sophisticated management systems in response to climate change. Thus, relying on predictive models and monitoring for timely warning can protect the health of the public.
Acknowledgments
This work was supported by a grant from the National Institute of Environmental Research (NIER-SP2018-309, TSK, OJR, and SSL) funded by the Ministry of Environment (MOE) of the Republic of Korea.
Footnotes
Conflict of Interests
The authors have no financial conflicts of interest to declare.
REFERENCES
- 1.Kovats S, Haines A. The potential health impacts of climate change: an overview. Med. War. 1995;11:168–178. doi: 10.1080/07488009508409236. [DOI] [PubMed] [Google Scholar]
- 2.McMichael AJ, Woodruff RE, Hales S. Climate change and human health: present and future risks. Lancet. 2006;367:859–869. doi: 10.1016/S0140-6736(06)68079-3. [DOI] [PubMed] [Google Scholar]
- 3.Min S-K, Zhang X, Zwiers F, Shiogama H, Tung Y-S, Wehner M. Multimodel detection and attribution of extreme temperature changes. J. Climate. 2013;26:7430. doi: 10.1175/JCLI-D-12-00551.1. [DOI] [Google Scholar]
- 4.Chung Y-S, Yoon M-B, Kim H-S. On climate variations and changes observed in South Korea. Climate Change. 2004;66:151–161. doi: 10.1023/B:CLIM.0000043141.54763.f8. [DOI] [Google Scholar]
- 5.Rogers DJ, Suk JE, Semenza JC. Using global maps to predict the risk of dengue in Europe. Acta Trop. 2013;129:1–14. doi: 10.1016/j.actatropica.2013.08.008. [DOI] [PubMed] [Google Scholar]
- 6.Anderson M, Sansoneti PJ, Marteyn S. Shigella diversity and changing landscape: insights for the twenty-first century. Front. Cell. Infect. Microbiol. 2016;6:45. doi: 10.3389/fcimb.2016.00045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Centers for Disease Control and Prevention. CDC Yellow book 2018: Health information for international travel. Oxford University Press; New York: 2017. [Google Scholar]
- 8.European Center for Disease Prevention and Control. Annual Epidemiological Report 2016 - Shigellaosis [Internet] ECDC; Stockholm: 2016. [Cited 2018 Feb. 16, Accessed Mar. 20, 2020]. Available from: https://ecdc.europa.eu/sites/portal/files/documents/Shigellosis-annual-epidemiological-report-for 2014_0.pdf . [Google Scholar]
- 9.Fratamico P, Smith J. Escherichia coli infections. In: Riemann H, Cliver D, editors. Food borne infections and intoxications. Elsevier science; New York: 2006. p. 3. [DOI] [Google Scholar]
- 10.Andino A, Hanning I. Salmonella enterica: survival, colonization, and virulence differences among serovar. ScientificWorldJournal. 2015;2015:520179. doi: 10.1155/2015/520179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lamas A, Miranda JM, Regal P, Vázquez B, Franco CM, Cepeda A. A comprehensive review of non-enterica subspecies of Salmonella enterica. Microbiol. Res. 2018;206:60–73. doi: 10.1016/j.micres.2017.09.010. [DOI] [PubMed] [Google Scholar]
- 12.Grimont PA, Weill FX. Antigenic Formulae of the Salmonella Serovars. WHO Collaborating Centers for Reference and research on Salmonella, Institute Pasteu; Paris, France: 2007. [Google Scholar]
- 13.Chahin A, Opal SM. Severe Pneumonia caused by Legionella pneumophila: differential diagnosis and therapeutic considerations. Infect. Dis. Clin. North Am. 2017;31:111–121. doi: 10.1016/j.idc.2016.10.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Qin J, Lupo ZQ. Legionella and Coxiella effectors: strength in diversity and activity. Nat. Rev. Microbiol. 2017;15:591–605. doi: 10.1038/nrmicro.2017.67. [DOI] [PubMed] [Google Scholar]
- 15.Sabria M, Yu VL. Hospital-acquired legionellosis: solutions for a preventable infection. Lancet Infect. Dis. 2002;2:368–373. doi: 10.1016/S1473-3099(02)00291-8. [DOI] [PubMed] [Google Scholar]
- 16.van der Stel A-X, Wönsten MMSM. Regulation of respiratory pathways in Campylobacterota: a review. Front. Microbiol. 2019;10:1719. doi: 10.3389/fmicb.2019.01719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ramees TP, Dhama K, Karthik K, Rathore RS, Kumar A, Saminathan M, et al. Arcobacter: an emerging food-borne zoonotic pathogen, its public health concerns and advances in diagnosis and control - a comprehensive review. Vet. Q. 2017;37:136–161. doi: 10.1080/01652176.2017.1323355. [DOI] [PubMed] [Google Scholar]
- 18.World Health Organization. World Health Organization 2000; Annual report on campylobacteriosis. World Health Organization; Copenhagen, Denmark: 2000. [Google Scholar]
- 19.Konkel ME, Monteville MR, Rivera-Amill V, Jones LA. The pathogenesis of Campylobacter jejuni-mediated enteritis. Curr. Issues Intest. Microbiol. 2001;2:55–71. [PubMed] [Google Scholar]
- 20.Reidl J, Klose KE. Vibrio cholera and cholera: out of the water and into the host. FEMS Microbiol. Rev. 2002;26:25–136. doi: 10.1111/j.1574-6976.2002.tb00605.x. [DOI] [PubMed] [Google Scholar]
- 21.Clemens JD, Nair GB, Ahmed T, Qadri F, Holmgren J. Cholera. Lancet. 2017;390:1539–1549. doi: 10.1016/S0140-6736(17)30559-7. [DOI] [PubMed] [Google Scholar]
- 22.Climate Institute. Water. [Accessed Aug. 17, 2019]. Available at: https://www.climate.org/topics/water.html .
- 23.Highsmith AK, Abshire RL. Evaluation of a most probable number technique for the enumeration of Psudomonas aeruginosa. Appl. Microbiol. 1975;30:596–601. doi: 10.1128/AEM.30.4.596-601.1975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Rice EW, Baird RB, Eaton AD, Clesceri LS. Standard Methods for the Examination of Water and Wastewater. 21st Ed. American Water Works Association; Denver, CO, USA: 2012. [Google Scholar]
- 25.Walter WG. APHA standard methods for the examination of water and wastewater. Health Lab. Sci. 1998;4:137. [PubMed] [Google Scholar]
- 26.Young JC, Clesceri LS, Kamhawy SM. Changes in the biochemical oxygen demand procedure in the 21st Ed. of Standard Methods for the Examination of Water and Wastewater. Water Environ. Res. 2005;77:404–410. doi: 10.1002/j.1554-7531.2005.tb00299.x. [DOI] [PubMed] [Google Scholar]
- 27.Jin YJ, Park YK, Cho MS, Lee ES, Park DS. New insight and matrics to understand the ontogeny and succession of Lactobacillus plantarum subsp. plantarum and Lactobacillus plantarum subsp. argentoratensis. Sci. Rep. 2018;8:6029. doi: 10.1038/s41598-018-24541-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.World Health Organization. Guidelines for Drinking-water Quality. 4th Ed. World Health Organization; Geneva, Switzerland: 2017. [Google Scholar]
- 29.Canter L. Nitrates in Groundwater. CRC Press; Boca Raton, Fla, USA: 1997. [Google Scholar]
- 30.Sidhu JPS, Toze SG. Human pathogens and their indicators in biosolids; A literature review. Environ. Int. 2009;35:187–201. doi: 10.1016/j.envint.2008.07.006. [DOI] [PubMed] [Google Scholar]
- 31.Pennsylvania Department of Environmental Protection (PA DEP) Pa. Code § 109.301. General monitoring requirements. 2019. [Accessed Jan. 17, 2020]. Online at: http://www.pacodeandbulletin.gov/Display/pacode?file=/secure/pacode/data/025/chapter109/s109.301.html&searchunitkeywords =water%2Cquality&origQuery=water%20quality&operator=OR&title=null .
- 32.Kalkan S, Altug SGG. Bio-indicator bacteria & environmental variables of the coastal zones: the example of the Güllük Bay, Aegean Sea, Turkey. Mar. Pollut. Bull. 2015;95:380–384. doi: 10.1016/j.marpolbul.2015.04.017. [DOI] [PubMed] [Google Scholar]
- 33.Corain L, Salmaso L. Multivariate and multistrata nonparametric tests: the NPC method. J. Mod. Appl. Stat. Methods. 2004;3:443–461. doi: 10.22237/jmasm/1099268160. [DOI] [Google Scholar]
- 34.Obiri-Danso K, Jones K. Distribution and seasonality of microbial indicators and thermophilic Campylobacters in two freshwater bathing sites on the River Lune in north-west England. J. Appl. Microbiol. 1999;87:822–832. doi: 10.1046/j.1365-2672.1999.00924.x. [DOI] [PubMed] [Google Scholar]
- 35.Semenza JC, Herbst S, Rechenburg A, Suk JE, Höser C, Schreiber C, et al. Climate change impact assessment of food- and waterborne disease. Crit. Rev. Environ. Sci. Technol. 2012;42:857–890. doi: 10.1080/10643389.2010.534706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Kim HJ, You JY, Park CJ. Review of sewage and sewage sludge treatment in Korea. Int. Proc. Chem. Biol. Environ. Eng. 2017;101:67–73. [Google Scholar]