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Article

Meteorological and Water Quality Factors Associated with Microbial Diversity in Coastal Water from Intensified Oyster Production Areas of Thailand

by
Saharuetai Jeamsripong
1,2,*,
Varangkana Thaotumpitak
1,2,
Saran Anuntawirun
2,
Nawaphorn Roongrojmongkhon
2 and
Edward R. Atwill
3
1
Research Unit in Microbial Food Safety and Antimicrobial Resistance, Department of Veterinary Public Health, Faculty of Veterinary Science, Chulalongkorn University, Bangkok 10330, Thailand
2
Department of Veterinary Public Health, Faculty of Veterinary Science, Chulalongkorn University, Bangkok 10330, Thailand
3
Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Water 2022, 14(23), 3838; https://doi.org/10.3390/w14233838
Submission received: 4 October 2022 / Revised: 22 November 2022 / Accepted: 22 November 2022 / Published: 25 November 2022
(This article belongs to the Special Issue Waterborne Pathogens—Threats to Water Quality)
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Abstract

:
Coastal pollution is of public health concern due to the possibility of bacterial contamination in aquaculture affecting health risk and seafood safety. This study determined the concentrations of total coliforms, fecal coliforms, E. coli, and V. parahaemolyticus and the presence of V. cholerae and Salmonella in water and measured water quality and meteorological factors from the major oyster cultivation areas in Thailand. The water samples (n = 363) were collected from Surat Thani (n = 60), Chanthaburi (n = 60), Trat (n = 60), Phetchaburi (n = 60), Chonburi (n = 63), and Phang Nga (n = 60) from February 2021 to January 2022. The prevalence of total coliforms (96.7%), fecal coliforms (60.6%), E. coli (22.9%), along with the prevalence of Salmonella (2.5%), Vibrio parahaemolyticus (74.9%), and V. cholerae (11.3%) were determined. Common Salmonella serovars were Othmarschen and Lamberhurst. The concentration of E. coli was significantly associated with dissolved oxygen and precipitation (p < 0.0001). Therefore, continuing microbiological monitoring and surveillance of water for coastal aquaculture is important to produce safe aquatic products. Furthermore, raising awareness of coastal pollution and seafood safety will help enhance sustainable coastal fisheries in Thailand.

1. Introduction

The trend of global marine capture has increased from 81 million tons (MT) in 2017 to 84 MT in 2018 [1]. Thailand is one of the 25 major global marine-producing countries and a major exporter of aquaculture products [1]. In 2021, the number of molluscan shellfish farms in Thailand was approximately 5000 operations yielding greater than 98,000 tons of production, which generated THB 4189 million [2]. Most oyster farms in Thailand are in the southern and eastern region due to environmental suitability for cultivation. In 2021, the Gulf of Thailand produced 13,101 tons of shellfish valued at THB 255.4 million, while the production from the Andaman Sea was less productive, producing 216 tons valued at THB 24.5 million [2]. Thus, the production of oysters in Thailand generates a major source of household income and economic benefits for coastal communities. Due to the high consumer demand for Thailand’s oysters, determination of additional geographical areas with appropriate water quality for oyster cultivation is needed in order to both meet consumer demand and maintain oyster food safety.
Disturbance of the global environment causes potential threats to aquatic ecosystems [3]. Most oyster production areas in Thailand are in proximity to coastal communities, and therefore cultivated oysters can be potentially contaminated with microbial pollution such as wastewater discharge and runoff from household activities and agricultural operations [4,5]. Shellfish are known to be contaminated with bacterial pathogens such as Salmonella, Shigella, and Vibrio, causing potential foodborne illnesses [6,7,8]. The presence of Salmonella in oysters was reported at 7.4% in the U.S., while a higher prevalence (30.6%) was recently found in Thailand [7,9]. Escherichia coli is generally used as a bacterial indicator for the determination of food and water microbial quality. Vibrio spp. are natural inhabitants in marine animals and estuarine environments; however, they can also function as important human pathogens [10]. For example, V. parahaemolyticus are the main pathogens causing acute gastroenteritis and sepsis in humans worldwide [11,12]. It is estimated that more than 84,000 people are infected with Vibrio per year in the United States [13].
The food safety of oysters can be impacted by microbial contamination of coastal environments used for shellfish cultivation, with factors such as rainfall and water chemistry influencing the processes and local concentrations of microbial contamination. For example, previous studies have indicated that rainfall, wind, and other weather-related factors significantly influence bacterial dispersion into the estuarine environment and bacterial accumulation in oysters due to runoff and freshwater fluxes of microbial contaminants into cultivation waters [14,15,16]. In addition, water quality parameters can directly affect bacterial loads in cultivation water due to promoting preferable niches for these bacterial species and their survivability. For example, salinity and water temperature were correlated with growth of E. coli and V. parahaemolyticus in aquatic ecosystems [17,18]. These processes help explain the seasonal variation of bacterial diversity in oysters and cultivation waters [19] and create the need for developing predictive models for excessive microbial pollution of oyster cultivation areas that can lead to oyster contamination and impacts on human health. Such models could be based on meteorological or water quality parameters to help guide the selection of new oyster growing regions in Thailand that produce microbiologically-safe oysters. To achieve such a model, monitoring and surveillance of meteorological and water quality parameters for oyster cultivation areas is urgently needed in order to enhance seafood safety and meet the strong demand for this seafood commodity; unfortunately, such predictive models have not been developed for Thailand’s oyster industry.
There are three primary oyster species being cultured commercially in Thailand: Saccostrea commercialis, Crassostrea lugubris, and C. belcheri, with the majority of oyster cultivation based on small-scale family-owned operations with high economic potential. In Thailand, Surat Thani, Chanthaburi, Trat, Phetchaburi, Chonburi, and Phang Nga are provinces that have a high density of coastal oyster production, with some regions eager to expand production [2]. Bacterial contamination in the marine environment at existing oyster cultivation sites raises serious concern given the limited existing areas for production of high quality, food-safe oysters. As a consequence, Thailand’s oyster industry is seeking advice on how to identify new regions and where to expand existing locations for culturing oysters from non-contaminated areas in order to reduce the risk of microbial contamination of raw oysters. Therefore, the objectives of this study were to characterize the spatiotemporal variation of fecal coliforms, E. coli, V. parahaemolyticus, V. cholerae, and Salmonella in coastal waters and cultivation sites from the six major oyster growing regions in Thailand and to build a statistical model using meteorological and water quality data that can predict the mean concentration of waterborne E. coli at these cultivation sites.

2. Materials and Methods

2.1. Oyster Production Area and Water Sample Collection

Oyster cultivation areas in Surat Thani (9°12′737″ N, 99°27′276″ E), Chanthaburi (8°22′587″ N, 98°35′846″ E), Trat (12°04′610″ N, 102°35′843″ E), Phetchaburi (13°15′843″ N, 99°59′299″ E), Chonburi (13°20′482″ N, 100°55′054″ E), and Phang Nga (8°22′587″ N, 98°35′846″ E) were selected because they are the major oyster cultivation sites in Thailand (Figure 1). A total of 363 coastal water samples, which can be either estuarine water or seawater depending on the sample site location, were collected from February 2021 to January 2022. Sixty water samples per province were collected from all provinces, except sixty-three water samples were collected from Chonburi.
The seasons are classified as rainy and non-rainy seasons according to the Thai Meteorological Department (www.tmd.go.th) (accessed on 1 August 2022). In each province, one-day sampling was performed during these two seasons, rainy (n = 180) and non-rainy season (n = 183). Sampling dates in rainy and non-rainy seasons were followed as Surat Thani (19 September 2021, 20 March 2021), Chanthaburi (3 October 2021, 24 April 2021), Trat (16 May 2021, 14 January 2022), Phetchaburi (1 June 2021, 27 October 2021), Chonburi (6 September 2021, 17 February 2021), and Phang Nga (28 June 2021, 08 February 2021). Approximately 200–250 mL of water was individually collected into a sterile polyethylene bottle at 0.3–0.5 m below the water surface, which is the depth of Thailand’s oyster cultivation by rack or stake methods in accordance with the guidelines of oyster cultivation [20]. The samples were stored in a cooler at less than 10 °C during transportation and delivered to the laboratory within 24 h for further microbiological determination.

2.2. Environmental Parameters

We measured onsite at the time of sampling ambient air temperature (°C), relative humidity (RH) (%), current wind speed (m/s), maximum wind gust (m/s), and average wind speed (m/s) using an anemometer (Kestrel 3000, Nielsen-Kellerman, PA, USA). Season (rainy and non-rainy), average daily precipitation (mm), highest and lowest daily air temperature (°C), and average daily RH (%) were retrieved from the nearest meteorological station of the Thai Meteorological Department. The meteorological stations’ data followed the World Meteorological Organization (WMO) index at Chonburi (459,201/48,459), Chanthaburi (480,301/48,481), Phetchaburi (465,201/48,465) Trat (501,201/48,501), Phang Nga (561,201/48,561), and Surat Thani (551,201/48,551). The water chemistry parameters, including pH, dissolved oxygen (DO) (mg/L), and conductivity (mS/cm) were measured onsite at each sampling event using pH, DO, and conductivity meters (Extech Instruments SDL100, SDL150, EC500, Nashua, NH, USA). The salinity of each water sample (ppt) was measured using a refractometer (ATAGO Salinity Refractometer MASTER-S/Millα, Tokyo, Japan).

2.3. Determination of Total Coliforms, Fecal Coliforms, and E. coli

The concentration of total coliforms, fecal coliforms, and E. coli in each water sample was determined using the most probable number (MPN) according to the U.S. of Food and Drug Administration (U.S. FDA) Bacteriological Analytical Manual (BAM) [21]. A total of 25 mL of water sample was individually added to 225 mL of Buffered Peptone Water (BPW) (Difco, Sparks, MD, USA) and then serially diluted across five dilutions (10−1–10−5) using lactose broth (Difco) in triplicate. The lactose broth tubes were incubated at 37 °C for 24 h to observe gas formation and turbidity. The turbid tubes with gas production were marked as positive for total coliforms. One loopful of suspension from the lactose broth was added into E. coli (EC) Broth (Difco). The samples were incubated overnight at 44.5 °C in a water bath to observe gas production and turbidity of the suspension. The tubes with turbidity and gas formation were positive for fecal coliforms, and these tubes were used for MPN calculation (MPN/mL) for each replicate based on the MPN calculator provided by the U.S. Environmental Protection Agency (https://mostprobablenumbercalculator.epa.gov/mpnForm) (accessed on 1 August 2022). The mean of MPN was then calculated with the detection limit of MPN estimate was 3 MPN/mL. A loopful of EC broth was streaked on Eosin Methylene Blue (EMB) (Difco) and MacConkey (Difco) agar plates, and the plates were incubated at 37 °C for 24 h. The presumptive colonies of E. coli can be distinguished by visual appearance, which are green-metallic sheen colonies on EMB agar plates and pink to dark pink colonies on MacConkey agar plates. Indole and catalase tests were used for biochemical confirmation for E. coli.

2.4. Determination of V. parahaemolyticus

Quantitative analysis of V. parahaemolyticus followed the standard U.S. FDA BAM method [22]. The water samples (25 mL) were added to 225 mL of Alkaline Peptone Water (APW) (Difco) in at least five consecutive dilutions from 10−1 to 10−5. The detection limit of V. parahaemolyticus concentration estimate was 3 MPN/mL. The APW tubes were incubated at 37 °C for 24 h. A loopful of turbid tubes were then streaked on Thiosulfate-Citrate-Bile Salts-Sucrose (TCBS) (Difco) agar. After overnight incubation at 37 °C, positive colonies of V. parahaemolyticus were steaked on CHROMagar™ Vibrio (HiMedia Laboratories Ltd., Mumbai, India) agar plates for confirmation. The plates were incubated at 37 °C for 24 h. Presumptive colonies of V. parahaemolyticus are green colonies on TCBS, and green blue to turquoise blue colonies on CHROMagar™ Vibrio agar plates. The suspected colonies of V. parahaemolyticus were biochemically confirmed using Tryptic Soy Agar (TSA) (Difco) supplemented with 2% NaCl and Triple Sugar Iron (TSI) test.

2.5. Identification of V. cholerae

Isolation of V. cholerae followed the standard method of U.S. FDA BAM [22]. A loopful of the APW suspension from a previous step of V. parahaemolyticus isolation was streaked to TCBS agar plate, and the plates were incubated at 37 °C overnight. Suspected colonies of V. cholerae were streaked onto CHROMagar™ Vibrio (HiMedia Laboratories Ltd.) agar plate. After overnight incubation at 37 °C, positive colonies of V. cholerae are yellow on TCBS agar plate. The suspected colonies of V. cholerae were biochemically confirmed using TSA supplemented with 2% NaCl and oxidase test.

2.6. Isolation and Serotyping of Salmonella spp.

Detection of Salmonella was performed according to the ISO standard and confirmed using the U.S. FDA’s BAM [23,24]. Approximately 25 mL of water samples was added to 225 mL of BPW, and the suspension was thoroughly mixed and incubated at 37 °C for 24 h. A 100 µL of BPW solution was transferred to Modified Semi-Solid Rappaport-Vassiliadis (MSRV) (Difco) agar plates. The plates were incubated overnight at 42 °C. A loopful of straw colonies at the site of inoculation was streaked to Xylose Lysine Deoxycholate (XLD) (Difco) agar plates and the plates were incubated at 37 °C for 24 h. The colonies of Salmonella on XLD agar are red colonies with or without a black center. TSI and citrate tests were used for biochemical confirmation.
At least three isolates per one positive Salmonella sample were serotyped using a slide-agglutination test according to Kauffman and White scheme [25] with available antisera (S&A Reagents Lab, Bangkok, Thailand).

2.7. Statistical Analysis

Descriptive statistics were used to examine pathogen diversity and meteorological variables. Independent t-test and chi-square test were used to compare the concentrations of E. coli and V. parahaemolyticus, and the presence of Salmonella and V. cholerae in the samples between rainy and non-rainy seasons, respectively. Prior to regression modeling, potential collinearity between environmental variables was evaluated by examining the variance inflation factor; all potential variables had values less than three, indicating high levels of collinearity were not present. A mixed-effects negative binomial regression model was then used based on over-dispersed count outcome variables to determine the association between the concentration of E. coli in cultivation water and various independent variables, such as quality of water and meteorological parameters. Forward selection and backward elimination were used to identify final regression models for waterborne E. coli. p-Values and confidence intervals were adjusted for potentially correlated data within location of sampling through the use of a location random effect. Likelihood ratio tests and p < 0.05 were applied for final selection of variables in the regression models. Stata software version 14 (StataCorp, College Station, TX, USA) was used for all statistical analyses.

3. Results

3.1. Meteorological Data

Overall, the averages (±standard deviation) (SD) for instantaneous wind speed, maximum wind gust, and average wind speed were 4.8 (±3.2) m/S, 7.2 (±3.9) m/S, and 3.5 (±2.8) m/S, respectively (Table 1). Average ambient air temperature was 30.8 (±2.4) °C and RH was 72.3 (±10.8) %. Phang Nga and Trat were the provinces with the highest average ambient air temperature of 32.3 °C. The lowest temperatures were 29.2 °C in Phetchaburi and 29.4 °C in Chonburi. The highest average RH of 77.5% was found in Phetchaburi, while the lowest average RH (66.8%) was observed in Surat Thani.
Generally, the average daily precipitation (4.9 ± 8.7 mm), the highest (33.1 ± 1.3 °C) and the lowest (24.2 ± 1.6 °C) air temperature, and RH (78.8 ± 6.7%) were measured by the Thailand Meteorological Department (Table 2). Phetchaburi had the highest average rainfall (18.3 mm) with the highest RH (89.5%), while the average lowest RH (60.0%) was reported in Phang Nga. The highest average daily air temperature was 35.6 °C in Trat, while the lowest average temperatures were in Chonburi (22.6 °C) and Trat (23.0 °C).

3.2. Water Chemistry Data

During this study, the average conductivity of water was 37.4 (±12.2) mS/cm (Table 3). Chanthaburi had the lowest electrical conductivity at 31.4 (±12.3) mS/cm, while Phang Nga contained the highest water conductivity at 42.1 (±5.2) mS/cm, followed by Trat at 41.1 (±4.1) mS/cm. The average DO level, salinity, and pH were 6.8 (±1.9) mg/L, 25.4 (±9.4) ppt, and 7.7 (±0.3), respectively. The average lowest DO levels were found in Chonburi (5.0 ± 0.8 mg/L) and Phetchaburi (5.5 ± 0.5 mg/L). The highest salinity levels were in Trat (32.2 ± 4.2 ppt), followed by Phang Nga (28.4 ± 3.6 ppt) and Surat Thani (26.8 ± 6.5 ppt).

3.3. Occurrence of Total Coliforms, Fecal Coliforms, and E. coli

Summarizing all water samples (n = 363), the overall prevalence for total coliforms, fecal coliforms, and E. coli was 96.7%, 60.6%, and 22.9%, respectively (Table 4). A hundred percent of water samples had total coliforms in Phang Nga. The highest prevalence of waterborne fecal coliforms and E. coli contamination were observed in more northern provinces of Chonburi (92.1%) and Chanthaburi (48.3%), while the lowest prevalence for waterborne E. coli were found in the more southern provinces of Surat Thani (3.3%) and Phang Nga (6.7%).
Average concentrations and SD of total coliforms (467.3 ± 1500.8 MPN/mL), fecal coliforms (22.5 ± 145.2 MPN/mL), and E. coli (16.3 ± 127.6 MPN/mL) are shown in Table 5. The highest average concentrations of total coliforms (966.1 ± 2255.8 MPN/mL), fecal coliforms (115.2 ± 335.1 MPN/mL), and E. coli (83.0 ± 299.0 MPN/mL) were observed in more northern Chonburi province; in contrast, the southern Trat and Phang Nga provinces had the lowest concentrations of waterborne bacterial indicators. Based on t-tests, the concentration of E. coli was significantly higher in the rainy season compared to the non-rainy season (p = 0.03).

3.4. Occurrence of V. parahaemolyticus and V. cholerae

The overall prevalence of V. parahaemolyticus was 74.9%, with the highest occurrences in Phang Nga (95.0%) and Trat (93.3%) (Table 4). The concentrations of waterborne V. parahaemolyticus exhibited large variation (3 to 11,000 MPN/mL), with an average concentration of 372.0 (±1298.8) MPN/mL (Table 5). The highest mean concentrations of V. parahaemolyticus were observed in the water collected from Phang Nga (1206.2 ± 2445.0 MPN/mL), followed by Chanthaburi (360.2 ± 1540.5 MPN/mL) and Surat Thani (220.3 ± 674.4 MPN/mL), while Phetchaburi had the lowest levels of V. parahaemolyticus (53.5 ± 270.2 MPN/mL). There was no significant difference in the concentration of V. parahaemolyticus between the rainy and non-rainy seasons (p = 0.45).
The overall prevalence of V. cholerae contamination in the water samples was 11.3% (Table 4), with the highest prevalence observed in Chanthaburi (33.3%) and Phetchaburi (16.7%), while only 1.7% of water samples from Trat had V. cholerae. The detection of V. cholerae was significantly associated with the rainy season (p < 0.0001).

3.5. Prevalence of Salmonella and Their Serovars

The overall prevalence of waterborne Salmonella spp. was 2.5% (n = 9) (Table 4), whereby positive water samples were limited to Chonburi (12.7%) and Chanthaburi (1.7%). The presence of Salmonella in the water samples was significantly associated with the rainy season (p = 0.002).
The distribution of serovars for the 27 Salmonella isolates from nine Salmonella-positive samples were as follows: Bolton (n = 2), Braenderup (n = 3), Bruebach (n = 3), Chester (n = 3), Lamberhurst (n = 4), Litchfield (n = 1), Orion (n = 1), Othmarschen (n = 6), Paratyphi B (n = 3), and Wentworth (n = 1).

3.6. Multivariable Mixed-Effects Negative Binomial Regression Models

The concentration of E. coli in coastal water samples from oyster growing regions for different provinces and rainy seasons were compared using negative binomial regression, with Phang Nga and non-rainy season designated as the reference groups (Table 6). Chonburi had the highest E. coli concentrations (e3.82 = 45.6-times higher than Phang Nga, p < 0.0001), followed by Chanthaburi (e1.12 = 3.1-times higher than Phang Nga, p = 0.002), while water from Trat had the lowest mean concentrations of E. coli (e−1.91 = 0.15-times lower than Phang Nga, p < 0.0001). E. coli contamination in the water samples collected from Phang Nga, Surat Thani, and Phetchaburi were not significantly different from each other. Concentrations of E. coli were 16.7 times higher during the rainy season compared to the dry season (e2.818 = 16.7, p = 0.001) (Table 6). High fluctuation of temperature during the rainy and non-rainy seasons was observed in Trat (4.2 °C) followed by Phetchaburi (3.3 °C) and Surat Thani (3.2 °C) (Figure 2).
A second negative binomial regression model was constructed for environmental and water quality parameters associated with concentrations of E. coli in these same coastal water samples (n = 363) from oyster production regions in Thailand. The DO level was negatively associated with E. coli, while the level precipitation was positively associated with the concentrations of E. coli in these coastal water samples (Table 7). Figure 3a,b are the mean E. coli concentrations predicted by the regression equation shown in Table 7, with all other variables set at their median values observed during the study when plotting the expected mean E. coli values as a function of a specific variable.

4. Discussion

In Thailand, oyster cultivation occurs in natural estuaries and near shore coastal environments, so the quality and food safety of oysters depends in part on water quality. Various locations in Thailand are suitable for oyster cultivation: Surat Thani and Phang Nga are located in the south; Chanthaburi, Trat, and Chonburi are located in the east; and Phetchaburi is located in central Thailand (Figure 1). The most famous and largest area for oyster cultivation is Surat Thani (6,158,912 m2), followed by Trat (1,332,816 m2) and Phetchaburi (921,664 m2) [2]. The main species of oyster cultivation in Thailand are Crassostrea belcheri, C. lugubris, and Saccostrea commercialis. The two formers are large oyster species which are generally cultivated in Phang Nga and Surat Thani, while the latter is a relatively small oyster that is widely distributed in Chanthaburi, Trat, Phetchaburi, and Chonburi provinces. Even though coastal aquaculture is essential for many Thai coastal communities, systematic surveillance of water quality for oyster aquaculture is lacking. Therefore, it is difficult to identify proper areas for oyster cultivation and harvest and to implement seafood safety measures consistent with levels of food safety risk.
According to Thai National Environment Board (NEB), the quality of seawater for aquaculture should have a pH ranging between 7.0 and 8.5, DO greater than 4 mg/L, and the alteration of seawater temperature not greater than 1 °C from natural conditions [26]. In this study, the overall average pH (7.7 ± 0.3) and DO level (6.8 ± 1.9 mg/L) were within the national standard requirement. For ambient air temperature, Phang Nga and Trat were the provinces with the highest temperature of 32.3 °C, while Chonburi and Phetchaburi had the lowest temperatures at 29.4 and 29.2 °C, respectively. The province with the highest temperature variation was Trat. A previous study indicated that oyster bacterial accumulation was associated with increases of temperature [27]. Similarly, the increased concentration of V. parahaemolyticus in oysters and estuarine water was associated with warmer conditions [28,29]. This may be because the alteration of temperature directly affected the oyster growth and plankton accumulation [30]. Thus, oyster cultivation areas with high coastal water temperature should be more frequently monitored for pathogen contamination.
Regarding bacterial diversity and co-occurrence of bacterial species in cultivation water, the overall prevalence of total coliforms, fecal coliforms, and E. coli were 96.7%, 60.6%, and 22.9%, respectively. Based on the Venn diagram (Figure 4), most of the water samples were positive for V. parahaemolyticus (n = 272), with 59 samples positive for both V. parahaemolyticus and E. coli, and 18 samples having these two bacteria in addition to V. cholerae. All Salmonella positive samples contained V. parahaemolyticus and E. coli, while 61 water samples were negative for all tested bacteria.
Chanthaburi was the leading province for E. coli contamination (48.3%). Average concentrations of total coliforms, fecal coliforms, and E. coli in water ranged from 101 to 102 MPN/mL. These levels of bacteria can be contrasted against regulations of maximum bacterial levels in oyster cultivation water. According to U.S. FDA, the National Shellfish Sanitation Program (NSSP) stipulates the level of fecal coliforms should be less than 49 MPN/100 mL in water for oyster cultivation. Regarding the Thailand NEB, the concentrations of total coliforms and fecal coliforms for coastal aquaculture should not exceed 1,000 and 70 CFU in 100 mL of cultivation water, respectively [26]. Given these standards, 60.6% (n = 220/363) of the samples exceeded the limit of the NSSP requirement, and 75.2% (n = 273/363) and 60.6% (n = 220/363) of the samples were greater than the Thailand NEB standard limits of total coliforms and fecal coliforms, respectively. Chonburi was the province with the highest number of total coliforms, fecal coliforms, and E. coli in the water (p < 0.0001), suggesting this area has either low sanitation and/or high bacterial inputs and/or extended bacterial survival. Phang Nga and Trat experienced higher air temperatures, which suggested we might observe higher E. coli contamination than any other provinces. However, the highest E. coli contamination was in Chonburi.
Oyster cultivation in Thailand is mostly located in proximity to anthropogenic activities such as household, community, waterside restaurant, fresh market, and other related activities leading to a higher possibility for bacterial contamination of nearby cultured oysters. For example, sewage and wastewater from human communities were confirmed as primary sources of bacterial contamination in Thailand’s coastal systems [31,32]. It should be noted that anthropogenic sources, along with fishery and shellfish aquaculture activities were major sources of coastal debris in Chonburi [33]. Therefore, identifying potential hotspots of bacterial contamination and examination of water quality for oyster cultivation should be performed regularly in order to decrease potential bacterial contamination and enhance seafood safety.
Both seasonal factors, water chemistry, and geographical factors were associated with the concentration of E. coli in oyster cultivation water in Thailand. Concentrations of E. coli in oyster cultivation water during the rainy season were nearly 17 (e2.818 = 16.7) times greater than those from the dry period, consistent with previous studies [34,35]. Based on mixed-effects negative binomial regression model, the mean concentration of E. coli was associated with DO and precipitation (Table 7). If these associations between E. coli, water chemistry, and meteorological parameters are sufficiently causal, this would suggest that altering such environmental parameters could impact coastal marine aquaculture, including oyster health and immunity, microbial growth, and pathogen virulence [36]. For example, the level of DO was negatively associated with the concentration of E. coli. For each additional one mg/L of DO, the concentration of E. coli in coastal water decreases 47% (e−0.630 = 0.53). It is postulated that high DO can generate oxidative stress for waterborne E. coli. Bacterial exposure to reactive oxygen species can cause non-physiological growth conditions [36,37]. Each additional millimeter of precipitation was associated with a 2.69-times increase in E. coli concentration (e0.99 = 2.69). This study postulated that precipitation was correlated with an increase in waterborne E. coli concentrations due to such processes as overland flow, sewage, and runoff into coastal environments leading to bacterial contamination, particularly when human communities, seafood markets, and waterline restaurants are near coastal aquaculture areas. Proper selection of locations for new or expanding coastal aquaculture is urgently needed, with stringent criteria needed for oyster cultivation sites so that food safety and high quality can be maintained.
V. parahaemolyticus is a free-living and halophilic bacterium found in coastal and estuarine environments. The highest occurrence V. parahaemolyticus was in Phang Nga (95.0%) and Trat (93.3%), which agreed with a previous study in Vietnam that 78.1% of water samples from aquaculture farms were positive for V. parahaemolyticus [38]. Similarly, the prevalence for V. parahaemolyticus was 63.4% in seafood, with the highest prevalence in oysters [39]. In this study, the overall mean concentration of V. parahaemolyticus was 372.0 ± 1,298.8 MPN/mL in the water, which was lower than the previous observation in seawater samples (1850 CFU/mL) from Delaware Bay [40]. The presence of V. cholerae was reported in this study at 11.3%, which raised a public health concern given that human cases of cholera can be found in locations that suffer from poor water quality and inadequate sanitation [41]. This finding agreed with a previous report on V. cholerae in seawater (13.2%) from South Korea [42]. In addition, it has been documented that elevations of V. cholerae and V. parahaemolyticus have been associated with increases in seawater temperatures [29,43,44]. Therefore, findings from this study suggest that long-term monitoring on meteorological and water quality parameters, along with surveillance of pathogens, are essential steps to produce better quality seafood products.
The overall prevalence of Salmonella in oyster cultivation water samples was 2.5%, which was limited to samples from Chonburi and Chanthaburi. The predominant Salmonella serovars observed in this study were Othmarschen (n = 6), followed by Lamberhurst (n = 4), Paratyphi B (n = 3), Braenderup (n = 3), Bruebach (n = 3), and Chester (n = 3). From previous outbreaks, serovar Othmarschen was presumed to come from food handlers and patients with severe fecal infections associated with gastroenteritis [45,46]. The distribution of S. Lamberhurst has been reported in seawater in Mexico and in fish and shrimp in India [47,48]. Recently, S. Lamberhurst was reported from swamp deer at the Kanpur Zoo in India [49]. To our knowledge, the serovar Othmarschen, Lamberhurst, and Bruebach have not been reported in Thailand. Salmonella serovars Braenderup, Chester, and Paratyphi B are associated with human outbreaks: S. Braenderup was implicated in multiple outbreaks from shell eggs in the U.S. and melon in European Union [50,51]; serovar Chester was reported in a rural community in Japan and seafood from Morocco [52,53]; S. Paratyphi B was responsible for outbreaks in the U.S. from imported raw tuna and was previously reported in estuarine environments from Thailand [7,54]. The distribution of Salmonella in the coastal environment may have a possibility to contaminate fish and fishery products causing potential threats to human health.

5. Conclusions

This study characterized meteorological parameters, water quality, and bacterial diversity in the cultivation water used for much of the oyster aquaculture occurring in Thailand. Significant associations between parameters for weather, water chemistry, and bacterial species were identified for waterborne E. coli. This study fostered an improved understanding and initial efforts at predictive regression modeling for estimating the likelihood of E. coli contamination in estuarine and seawater for oyster cultivation in Thailand. The benefits of using predictive statistical models for bacterial contamination of cultivation water are to provide an early warning system for seafood safety. Further identification of potential terrestrial sources of microbial pollution for cultivation water by using molecular epidemiological tools is needed for selection of proper locations for new or expanding mariculture. Farm biosecurity, good aquaculture practices, and seafood consumption using appropriate time and temperature for cooking should be implemented to enhance seafood safety in Thailand.

Author Contributions

Conceptualization, E.R.A. and S.J.; methodology, S.A., V.T., N.R. and S.J.; validation, S.J. and E.R.A.; formal analysis, S.J.; investigation, S.J.; resources, S.J.; funding acquisition, S.J.; writing—original draft preparation, S.A., V.T., N.R. and S.J.; writing—review and editing, S.J.; supervision, S.J. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by Thailand Science Research and Innovation (TSRI), grant number CU_FRB640001_01_31_9.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Piyachat Dachasri from Surat Thani Coastal Fisheries Research and Development Center, Nantapon Suksamran from Chanthaburi Coastal Fisheries Development and Research Center, Krissadakorn Hemwech from Trat Coastal Aquaculture Research and Development Center, Teerapong Banleng from Phetchaburi Coastal Aquaculture Research and Development Center, Somporn Sarakarn from Ban Khok Krai Community Enterprise in Phang Nga, and Alongot Intarachart and Attawut Kantavong from Sriracha Fisheries Research Station, Faculty of Fisheries, Kasetsart University for sample collection, and Jarukorn Sripradite for laboratory assistance. This project has been reviewed and certified by Chulalongkorn University Faculty of Veterinary Science Biosafety Committee (IBC 2131029).

Conflicts of Interest

The authors declare no conflict of interest.

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Water 14 03838 g001 550
Figure 1. Geographical distribution of water sampling locations from the high-density areas of oyster cultivation in (A) Chonburi, (B) Chanthaburi, (C) Phetchaburi, (D) Trat, (E) Phang Nga, and (F) Surat Thani provinces (n = 363).
Figure 1. Geographical distribution of water sampling locations from the high-density areas of oyster cultivation in (A) Chonburi, (B) Chanthaburi, (C) Phetchaburi, (D) Trat, (E) Phang Nga, and (F) Surat Thani provinces (n = 363).
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Figure 2. The average temperature during rainy (n = 180) and non-rainy season (n = 183), stratified by province.
Figure 2. The average temperature during rainy (n = 180) and non-rainy season (n = 183), stratified by province.
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Water 14 03838 g003 550
Figure 3. Predicted mean concentration of E. coli (MPN/mL) in coastal water samples from oyster production regions in Thailand as a function of: (a) level of DO (mg/L); (b) level of precipitation (mm).
Figure 3. Predicted mean concentration of E. coli (MPN/mL) in coastal water samples from oyster production regions in Thailand as a function of: (a) level of DO (mg/L); (b) level of precipitation (mm).
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Water 14 03838 g004 550
Figure 4. The presence of E. coli, Salmonella, V. parahaemolyticus, and V. cholerae in coastal water samples (n = 363) from oyster production regions in Thailand.
Figure 4. The presence of E. coli, Salmonella, V. parahaemolyticus, and V. cholerae in coastal water samples (n = 363) from oyster production regions in Thailand.
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Table
Table 1. Average and standard deviation of ambient air environmental parameters measured onsite at the time of sampling, stratified by province, from 2021–2022.
Table 1. Average and standard deviation of ambient air environmental parameters measured onsite at the time of sampling, stratified by province, from 2021–2022.
ProvinceAverage (±SD)
Current Wind Speed (m/s)Maximum Wind Gust (m/s)Average Wind Speed (m/s)Air Temperature (°C)Relative
Humidity (%)
Surat Thani (n = 60)6.3 (4.2)8.5 (4.9)4.3 (1.7)31.6 (2.5)66.8 (11.1)
Chanthaburi (n = 60)6.7 (4.0)10.5 (3.5)5.1 (3.6)30.4 (1.9)73.2 (16.8)
Trat (n = 60)3.1 (1.2)4.6 (1.7)3.4 (1.4)32.3 (2.6)70.4 (8.9)
Phetchaburi (n = 60)3.9 (2.2)7.2 (3.0)1.4 (0.4)29.2 (2.0)77.5 (8.1)
Chonburi (n = 63)5.1 (1.9)6.0 (1.9)4.3 (1.7)29.4 (1.1)76.6 (4.2)
Phang Nga (n = 60)3.7 (3.1)6.3 (4.3)1.4 (0.8)32.3 (1.8)69.3 (7.0)
Overall average (n = 363)4.8 (3.2)7.2 (3.9)3.5 (2.8)30.8 (2.4)72.3 (10.8)
Table
Table 2. Average and SD of daily weather data retrieved from the Meteorological Department from 2021–2022.
Table 2. Average and SD of daily weather data retrieved from the Meteorological Department from 2021–2022.
ProvinceAverage (±SD)
Precipitation (mm)Highest Temperature (°C)Lowest Temperature (°C)Relative Humidity (%)
Surat Thani (n = 60)1.4 (1.4)32.2 (0.1)23.9 (0.5)80.5 (3.5)
Chanthaburi (n = 60)9.6 (10.1)32.5 (0.9)26.1 (0.6)75.2 (5.5)
Trat (n = 60)NA35.6 (0.1)23.0 (0.5)77.5 (3.5)
Phetchaburi (n = 60)18.3 (8.2)32.2 (0.2)24.9 (0.1)89.5 (2.5)
Chonburi (n = 63)NA33.2 (0.7)22.6 (2.6)74.0 (7.1)
Phang Nga (n = 60)NA33.2 (0.1)24.7 (0.4)60.0 (1.5)
Overall average (n = 363)4.9 (8.7)33.1 (1.3)24.2 (1.6)78.8 (6.7)
Note: NA: not available data.
Table
Table 3. Average and SD of coastal water quality parameters, stratified by province.
Table 3. Average and SD of coastal water quality parameters, stratified by province.
ProvinceAverage (±SD)
Conductivity (mS/cm)DO 1 (mg/L)Salinity (ppt)pH
Surat Thani (n = 60)39.2 (8.1)8.6 (2.2)26.8 (6.5)7.9 (0.4)
Chanthaburi (n = 60)31.4 (12.3)6.6 (2.0)17.8 (9.9)7.3 (0.2)
Trat (n = 60)41.1 (4.1)7.7 (0.5)32.2 (4.2)7.8 (0.1)
Phetchaburi (n = 60)33.7 (13.4)5.5 (0.5)22.5 (9.6)7.8 (0.2)
Chonburi (n = 63)39.0 (16.3)5.0 (0.8)24.7 (12.1)7.6 (0.4)
Phang Nga (n = 60)42.1 (5.2)7.7 (1.0)28.4 (3.6)7.5 (0.2)
Overall average (n = 363)37.4 (12.2)6.8 (1.9)25.4 (9.4)7.7 (0.3)
Note: 1 DO: Dissolved oxygen.
Table
Table 4. Prevalence of total coliforms, fecal coliforms, E. coli, Salmonella, V. parahaemolyticus, and V. cholerae in coastal water samples (n = 363) from oyster production regions in Thailand.
Table 4. Prevalence of total coliforms, fecal coliforms, E. coli, Salmonella, V. parahaemolyticus, and V. cholerae in coastal water samples (n = 363) from oyster production regions in Thailand.
ProvinceNo. of Positive (%)
Total
Coliforms		Fecal
Coliforms		E. coliSalmonellaV. parahaemolyticusV. cholerae
Surat Thani (n = 60)58 (96.7)38 (63.3)2 (3.3)0 (0)42 (70.0)2 (3.3)
Chanthaburi (n = 60)57 (95.0)38 (63.3)29 (48.3)1 (1.7)42 (70.0)20 (33.3)
Trat (n = 60)59 (98.3)14 (23.3)5 (8.3)0 (0)56 (93.3)1 (1.7)
Phetchaburi (n = 60)58 (96.7)37 (61.7)22 (36.7)0 (0)30 (50.0)10 (16.7)
Chonburi (n = 63)59 (93.7)58 (92.1)21 (33.3)8 (12.7)45 (71.4)6 (9.5)
Phang Nga (n = 60)60 (100.0)35 (58.3)4 (6.7)0 (0)57 (95.0)2 (3.3)
Overall average (n = 363)351 (96.7)220 (60.6)83 (22.9)9 (2.5)272 (74.9)41 (11.3)
Note: The values in the brackets indicate the prevalence of pathogens.
Table
Table 5. Mean concentration and SD of total coliforms, fecal coliforms, E. coli, and V. parahaemolyticus in coastal water samples (n = 363) from oyster production regions in Thailand.
Table 5. Mean concentration and SD of total coliforms, fecal coliforms, E. coli, and V. parahaemolyticus in coastal water samples (n = 363) from oyster production regions in Thailand.
ProvinceMean Concentration (±SD) of Pathogens (MPN/mL)
Total ColiformsFecal ColiformsE. coliV. parahaemolyticus
Surat Thani (n = 60)94.9 (300.0)2.0 (1.5)1.6 (1.5)220.3 (674.4)
Chanthaburi (n = 60)936.8 (2155.5)6.7 (14.8)5.5 (14.2)360.2 (1540.5)
Trat (n = 60)512.5 (1629.5)1.6 (5.7)0.3 (0.9)251.8 (642.1)
Phetchaburi (n = 60)133.1 (209.8)3.2 (5.9)2.0 (5.0)53.5 (270.2)
Chonburi (n = 63)966.1 (2255.8)115.2 (335.1)83.0 (299.0)151.2 (387.3)
Phang Nga (n = 60)136.5 (423.5)1.9 (1.8)1.8 (1.8)1,206.2 (2445.0)
Overall average (n = 363)467.3 (1500.8)22.5 (145.2)16.3 (127.6)372.0 (1298.8)
Note: The mean concentrations and SD of these pathogens took into consideration all water samples.
Table
Table 6. Negative binomial regression model of the E. coli concentrations as a function of province and season in coastal water samples (n = 363) from oyster production regions in Thailand.
Table 6. Negative binomial regression model of the E. coli concentrations as a function of province and season in coastal water samples (n = 363) from oyster production regions in Thailand.
PredictorCoefficientStd. Err. 1C.I. 2p-Value
Province
Phang Nga (n = 60)Reference group
Chonburi (n = 63)3.8230.3613.117–4.530<0.0001
Surat Thani (n = 60)−0.1250.378−0.867–0.6160.741
Chanthaburi (n = 60)1.1150.3680.393–1.8370.002
Trat (n = 60)−1.9050.441−2.769–(−1.040)<0.0001
Phetchaburi (n = 60)0.1030.376−0.633–0.8390.784
Intercept0.5950.2660.073–1.1170.025
Season
Non-rainy (n = 183)Reference group
Rainy (n = 180)2.8180.8191.211–4.4240.001
Intercept0.6130.2900.044–1.8120.035
Note: 1 Std. Err.: standard error; 2 C.I.: confidence interval.
Table
Table 7. Mixed-effects negative binomial regression model for environmental and water quality factors associated with the concentration of E. coli in coastal water samples (n = 363) from oyster production regions in Thailand.
Table 7. Mixed-effects negative binomial regression model for environmental and water quality factors associated with the concentration of E. coli in coastal water samples (n = 363) from oyster production regions in Thailand.
PredictorCoefficientRobust Std. Err. 1C.I. 2p-Value
DO 3 (mg/L)−0.6300.127−0.880–(−0.380)<0.0001
Precipitation (mm)0.0990.0280.043–0.154<0.0001
Intercept4.5640.7982.999–6.129<0.0001
Note: AIC 4: 1,313.099. 1 Robust Std. Err.: robust standard error; 2 C.I.: confidence interval; 3 DO: dissolved oxygen; 4 AIC: Akaike Information Criteria.
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Jeamsripong, S.; Thaotumpitak, V.; Anuntawirun, S.; Roongrojmongkhon, N.; Atwill, E.R. Meteorological and Water Quality Factors Associated with Microbial Diversity in Coastal Water from Intensified Oyster Production Areas of Thailand. Water 2022, 14, 3838. https://doi.org/10.3390/w14233838

AMA Style

Jeamsripong S, Thaotumpitak V, Anuntawirun S, Roongrojmongkhon N, Atwill ER. Meteorological and Water Quality Factors Associated with Microbial Diversity in Coastal Water from Intensified Oyster Production Areas of Thailand. Water. 2022; 14(23):3838. https://doi.org/10.3390/w14233838

Chicago/Turabian Style

Jeamsripong, Saharuetai, Varangkana Thaotumpitak, Saran Anuntawirun, Nawaphorn Roongrojmongkhon, and Edward R. Atwill. 2022. "Meteorological and Water Quality Factors Associated with Microbial Diversity in Coastal Water from Intensified Oyster Production Areas of Thailand" Water 14, no. 23: 3838. https://doi.org/10.3390/w14233838

APA Style

Jeamsripong, S., Thaotumpitak, V., Anuntawirun, S., Roongrojmongkhon, N., & Atwill, E. R. (2022). Meteorological and Water Quality Factors Associated with Microbial Diversity in Coastal Water from Intensified Oyster Production Areas of Thailand. Water, 14(23), 3838. https://doi.org/10.3390/w14233838

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