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Systematic review of predictive models of microbial water quality at freshwater recreational beaches.

ABSTRACT: Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18-24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.

SUBMITTER: Heasley C

PROVIDER: S-EPMC8389397 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent studies showing an association between fecal indicator organisms (FIOs) in sand and gastrointestinal (GI) illness among beachgoers with sand contact have important public health implications because of the large numbers of people who recreate at beaches and engage in sand contact activities. Yet, factors that influence fecal pollution in beach sand remain unclear. During the 2007 National Epidemiological and Environmental Assessment of Recreational (NEEAR) Water Study, sand samples were collected at three locations (60 m apart) on weekend days (Sat, Sun) and holidays between June and September at two marine beaches - Fairhope Beach, AL and Goddard Beach, RI - with nearby publicly-owned treatment works (POTWs) outfalls. F(+) coliphage, enterococci, Bacteroidales, fecal Bacteroides spp., and Clostridium spp. were measured in sand using culture and qPCR-based calibrator-cell equivalent methods. Water samples were also collected on the same days, times and transects as the 144 sand samples and were assayed using the same FIO measurements. Weather and environmental data were collected at the time of sample collection. Mean FIO concentrations in sand varied over time, but not space. Enterococci CFU and CCE densities in sand were not correlated, although other FIOs in sand were. The strongest correlation between FIO density in sand and water was fecal Bacteroides CCE, followed by enterococci CFU, Clostridium spp. CCE, and Bacteroidales CCE. Overall, the factors associated with FIO concentrations in sand were related to the sand-water interface (i.e., sand-wetting) and included daily average densities of FIOs in water, rainfall, and wave height. Targeted monitoring that focuses on daily trends of sand FIO variability, combined with information about specific water quality, weather, and environmental factors may inform beach monitoring and management decisions to reduce microbial burdens in beach sand. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

Project description:BackgroundFecal bacterial densities are proxy indicators of beach water quality, and beach posting decisions are made based on Beach Action Value (BAV) exceedances for a beach. However, these traditional beach monitoring methods do not reflect the full extent of microbial water quality changes associated with BAV exceedances at recreational beaches (including harmful cyanobacteria). This proof of concept study evaluates the potential of metagenomics for comprehensively assessing bacterial community changes associated with BAV exceedances compared to non-exceedances for two urban beaches and their adjacent river water sources.ResultsCompared to non-exceedance samples, BAV exceedance samples exhibited higher alpha diversity (diversity within the sample) that could be further differentiated into separate clusters (Beta-diversity). For Beach A, Cyanobacterial sequences (resolved as Microcystis and Pseudanabaena at genus level) were significantly more abundant in BAV non-exceedance samples. qPCR validation supported the Cyanobacterial abundance results from metagenomic analysis and also identified saxitoxin genes in 50% of the non-exceedance samples. Microcystis sp and saxitoxin gene sequences were more abundant on non-exceedance beach days (when fecal indicator data indicated the beach should be open for water recreational purposes). For BAV exceedance days, Fibrobacteres, Pseudomonas, Acinetobacter, and Clostridium sequences were significantly more abundant (and positively correlated with fecal indicator densities) for Beach A. For Beach B, Spirochaetes (resolved as Leptospira on genus level) Burkholderia and Vibrio sequences were significantly more abundant in BAV exceedance samples. Similar bacterial diversity and abundance trends were observed for river water sources compared to their associated beaches. Antibiotic Resistance Genes (ARGs) were also consistently detected at both beaches. However, we did not observe a significant difference or correlation in ARGs abundance between BAV exceedance and non-exceedance samples.ConclusionThis study provides a more comprehensive analysis of bacterial community changes associated with BAV exceedances for recreational freshwater beaches. While there were increases in bacterial diversity and some taxa of potential human health concern associated with increased fecal indicator densities and BAV exceedances (e.g. Pseudomonas), metagenomics analyses also identified other taxa of potential human health concern (e.g. Microcystis) associated with lower fecal indicator densities and BAV non-exceedances days. This study can help develop more targeted beach monitoring strategies and beach-specific risk management approaches.

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Systematic review of predictive models of microbial water quality at freshwater recreational beaches.

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