Microbial community, pathogenic bacteria and high-risk anti-biotic resistance genes at two tropical coastal beaches adjacent to villages in Hainan, China.

OBJECTIVE: The aim of the study was to explore the correlation between characteristics of microbial community, pathogenic bacteria and high-risk antibiotic-resistant genes, between coastal beaches and a multi-warm-blooded host, as well as to determine potential species biomarkers for faecal source contamination on tropical coastal beaches in China. MATERIAL AND METHODS: The 'One-Health' approach was used in a microbiological study of beaches and warm-blooded hosts. The microbial.community was analyzed using 16S rRNA gene amplicons and shotgun metagenomics on Illumina NovaSeq. RESULTS: The Chao, Simpson, Shannon, and ACE indices of non-salt beach were greater than those of salt beaches at the genus and OTU levels (P < 0.001). Bacteroidota, Halanaerobiaeota, Cyanobacteria, and Firmicutes were abundant on salt beaches (P<0.01). Human-sourced microorganisms were more abundant on salt beaches, which accounted for 0.57%. Faecalibacterium prausnitzii and Eubacterium hallii were considered as reliable indicators for the contamination of human faeces. High-risk carbapenem-resistant Klebsiella pneumoniae and the genotypes KPC-14 and KPC-24 were observed on salt beaches. Tet(X3)/tet(X4) genes and four types of MCR genes co-occurred on beaches and humans; MCR9.1 accounted for the majority. Tet(X4) found among Cyanobacteria. Although rarely reported at Chinese beaches, pathogens, such as Vibrio vulnificus, Legionella pneumophila, and Helicobacter pylori, were observed. CONCLUSIONS: The low microbial community diversity, however, did not indicate a reduced risk. The transfer of high-risk ARGs to extreme coastal environments should be given sufficient attention.

Development of Predictive Models for "Very Poor" Beach Water Quality Gradings Using Class-Imbalance Learning.

Statistical water quality forecast models are useful tools to assist with beach management. In particular, multiple linear regression (MLR) models have been successfully developed for prediction of fecal indicator bacteria concentrations for beaches in river, lake, and marine environments. Nevertheless, an unresolved challenging issue is the reliable prediction of infrequent events of high bacterial concentrations to inform beach closure decisions to protect public health. The number of field data available for the infrequent events is typically an order of magnitude less than that for days when the water quality criterion is met-MLR models often perform poorly in predicting bacterial concentrations on days when the beaches should be closed. For beach management in Hong Kong, MLR models have been developed to predict beach water quality indices in terms of four gradings (BWQI-1 to 4) based on Escherichia coli (E. coli) concentrations. In this study, we propose an artificial intelligence (AI)-based binary classification (EasyEnsemble) model using class-imbalance learning to predict "very poor" occasions (BWQI-4)-when E. coli concentration exceeds 610 counts/100 mL. Models are developed for three marine beaches with different hydrographic and pollution characteristics using a 30 year data set spanning three periods with different water quality status. The model-data comparison over a wide range of conditions shows that the proposed method results in a significant improvement in the prediction of "very poor" water quality. The proposed class-imbalance method for predicting rare events has an F-score of 0.84, and it significantly outperforms MLR and classification tree (CT) models with corresponding F-scores of 0.39 and 0.69. A robust beach water quality forecast system can hence be developed using hybrid MLR-binary classification modeling.

Effects of Rainfall on Microbial Water Quality on Haeundae and Gwangan Swimming Beach

The associations between storm events, urban runoff and costal water quality have not been well investigated in Korea. A temporal and spatial analysis during summer, 2015 was conducted to determine associates between urban runoff and fecal indicator bacteria (Escherichia coli, Enterococcus) levels at two popular coastal beaches (Gwanganri beach and Haundae beach) in Busan. In this study, a clear relationship between rainfall and elevated number of indicators was observed. Two beaches met the costal beach water health standards after less than 3.0 mm of rain. Only for storms less than 2.5 mm was no observable rainfall effect. Our results revealed that exceedances were greatest in 5 hours following 41.0~45.5 rainfall, then declined the bacterial concentrations in 8 hours after the storm and they generally returned to levels below water health standards within 10~14 hours. But it took 2.7 days to get the level of water quality of dry days. The time required for water quality recovery depends on the intensity and duration of rainfall. In the event of intense rainfall issuance of beach closure by public authorities is warranted to protect public health.

Predicting water quality at Santa Monica Beach: evaluation of five different models for public notification of unsafe swimming conditions.

Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take ∼24 h and water quality conditions can change dramatically in that time, so unsafe conditions cannot presently be identified in a timely manner. Statistical, data-driven predictive models use information on environmental conditions (i.e., rainfall, turbidity) to provide nowcasts of FIB concentrations. Their ability to predict real time FIB concentrations can make them more accurate at identifying unsafe conditions than the current method of using day or older FIB measurements. Predictive models are used in the Great Lakes, Hong Kong, and Scotland for beach management, but they are presently not used in California - the location of some of the world's most popular beaches. California beaches are unique as point source pollution has generally been mitigated, the summer bathing season receives little to no rainfall, and in situ measurements of turbidity and salinity are not readily available. These characteristics may make modeling FIB difficult, as many current FIB models rely heavily on rainfall or salinity. The current study investigates the potential for FIB models to predict water quality at a quintessential California Beach: Santa Monica Beach. This study compares the performance of five predictive models, multiple linear regression model, binary logistic regression model, partial least square regression model, artificial neural network, and classification tree, to predict concentrations of summertime fecal coliform and enterococci concentrations. Past measurements of bacterial concentration, storm drain condition, and tide level are found to be critical factors in the predictive models. The models perform better than the current beach management method. The classification tree models perform the best; for example they correctly predict 42% of beach postings due to fecal coliform exceedances during model validation, as compared to 28% by the current method. Artificial neural network is the second best model which minimizes the number of incorrect beach postings. The binary logistic regression model also gives promising results, comparable to classification tree, by adjusting the posting decision thresholds to maximize correct beach postings. This study indicates that predictive models hold promise as a beach management tool at Santa Monica Beach. However, there are opportunities to further refine predictive models.