Comparisons of statistical models to predict fecal indicator bacteria concentrations enumerated by qPCR- and culture-based methods

A Photonic Immunosensor Detection Method for Viable and Non-Viable E. coli in Water Samples

Detection and enumeration of coliform bacteria using traditional methods and current molecular techniques against E. coli usually involve long processes with less sensitivity and specificity to distinguish between viable and non-viable bacteria for microbiological water analysis. This approach involves developing and validating an immunosensor comprising ring resonators functionalized with specific antibodies surrounded by a network of microchannels as an alternative method for detecting and indirectly enumerating Escherichia coli in samples of water for consumption. Different ELISA assays were conducted to characterize monoclonal and polyclonal antibodies selected as detection probes for specific B-galactosidase enzymes and membrane LPS antigens of E. coli. An immobilization control study was performed on silicon nitride surfaces used in the immunosensor, immobilized with the selected antibodies from the ELISA assays. The specificity of this method was confirmed by detecting as few as 10 CFU/mL of E. coli from viable and non-viable target bacteria after applying various disinfection methods to water samples intended for human consumption. The 100% detection rate and a 100 CFU/mL Limit of Quantification of the proposed method were validated through a comprehensive assessment of the immunosensor-coupled microfluidic system, involving at least 50 replicates with a concentration range of 10 to 106 CFU/mL of the target bacteria and 50 real samples contaminated with and without disinfection treatment. The correlation coefficient of around one calculated for each calibration curve obtained from the results demonstrated sensitive and rapid detection capabilities suitable for application in water resources intended for human consumption within the food industry. The biosensor was shown to provide results in less than 4 h, allowing for rapid identification of microbial contamination crucial for ensuring water monitoring related to food safety or environmental diagnosis and allowing for timely interventions to mitigate contamination risks. Indeed, the achieved setup facilitates the in situ execution of laboratory processes, allowing for the detection of both viable and non-viable bacteria, and it implies future developments of simultaneous detection of pathogens in the same contaminated sample.

Integrating culture-based and molecular methods provides an improved assessment of microbial quality in a coastal lagoon

Faecal pollution in aquatic environments is a worldwide public health concern, yet the reliability and comprehensiveness of the methods used to assess faecal contamination are still debated. We compared three approaches, namely a culture-based method to enumerate Faecal Indicator Bacteria (FIB), a FIB-targeting qPCR assay, and High-Throughput Sequencing (HTS) to detect faeces- and sewage-associated taxa in water and sediment samples of an impacted model lagoon and its adjacent sea across one year. Despite at different levels, all approaches agreed in showing a higher contamination in the lagoon than in the sea, and higher in sediments than water. FIB significantly correlated when considering separately sediment and water, and when using both cultivation and qPCR. Similarly, FIB correlated between cultivation and qPCR, but qPCR provided consistently higher estimates of FIB. Faeces-associated bacteria positively correlated with cultivated FIB in both compartments, whereas sewage-associated bacteria did only in water. Considering their benefits and limitations, we conclude that, in our study site, improved quali-quantitative information on contamination is provided when at least two approaches are combined (e.g., cultivation and qPCR or HTS data). Our results provide insights to move beyond the use of FIB to improve faecal pollution management in aquatic environments and to incorporate HTS analysis into routine monitoring.

Hydrological response of a paired watershed to rainfall storm events in arid region: a study in Dehgin of Hormozgan province, Iran

Influenced by global climate change, rainfall characteristics have changed in recent years, especially in arid regions. However, the actual response of the watersheds' hydrological indicators to erosive rainfalls has not been understood yet. Therefore, this study sought to investigate the changes in the watersheds' hydrological response due to the intra-rainfall indices of various events in the Dehgin paired watershed in Hormozgan, Iran, using the data collected from 2007 to 2019. To this end, first, all rainfall events which elicited the response of watersheds were identified concurrently based on the meteorological and flume data. Then, the qualitative and quantitative intra-storm variations associated with hydrological indicators (i.e., total runoff and discharge variables) were extracted for statistical analysis. The study's results revealed that (1) the increasing rates of the continuous discharge time of the control and treatment watershed's front position were about 2.25 and 4.76 times the rear position, respectively. (2) A strong linear relationship was found between total runoff volume and variables including precipitation of storm and time to storm peak in both control and treatment watersheds, with the R2 reported to be 67.7 and 63.6%, respectively. (3) It was also found that the watershed management operations reduced the variables' values including the maximum discharge, the discharge variation coefficient, continuity time of discharge, the peak of the time to discharge, the number of discharge peaks, and the total runoff volume by 3, 1.6, 2.25, 2.37, 2.42, and 3.27 times, respectively. Therefore, it could conceivably be argued that extreme rainfalls can be controlled by management practices in the watersheds.

Integrating culture and molecular quantification of microbial contaminants into a predictive modeling framework in a low-lying, tidally-influenced coastal watershed

Examinations of stormwater delivery in the context of tidal inundation are lacking. Along the coastal plains of the southeastern United States, tidal inundation is increasing in frequency and severity, often with dramatic adverse impacts on timely stormwater discharge, coastal flooding hazards, and even "sunny day flooding". Therefore, a comprehensive study was conducted to examine tidally-influenced stormwater outfalls discharging to Taylor's Creek, an estuary proximal to Beaufort, NC used regularly for recreation and tourism. Over a wide range of meteorological conditions, water samples were collected and analyzed for fecal indicator bacteria (FIB, used for water quality management) and previously published quantitative microbial source tracking (qMST) markers. Nineteen sampling events were conducted from July 2017-June 2018 with samples classified according to tidal state and defined as either inundated, receding, or transition. A first-of-its-kind multiple linear regression model was developed to predict concentrations of Enterococcus sp. by tidal cycle, salinity and antecedent rainfall. We demonstrated that the majority of variability associated with the concentration of Enterococcus sp. could be predicted by E. coli concentration and tidal phase. FIB concentrations were significantly (<0.05) influenced by tide with higher concentrations observed in samples collected during receding (low) tides (EC: log 3.12 MPN/100 mL; ENT: 2.67 MPN/100 mL) compared to those collected during inundated (high) (EC: log 2.62 MPN/100 mL; ENT: 2.11 MPN/100 mL) or transition (EC: log 2.74 MPN/100 mL; ENT: 2.53 MPN/100 mL) tidal periods. Salinity, was also found to significantly (<0.05) correlate with Enterococcus sp. concentrations during inundated (high) tidal conditions (sal: 17 ppt; ENT: 2.04 MPN/100 mL). Tide, not precipitation, was shown to be a significant driver in explaining the variability in Enterococcus sp. concentrations. Precipitation has previously been shown to be a driver of Enterococcus sp. concentrations, but our project demonstrates the need for tidal parameters to be included in the future development of water quality monitoring programs.

Modelling the impact of weather parameters on the microbial quality of water in distribution systems

In this study, a framework for integrating weather variables and seasons into the modelling and prediction of the microbial quality in drinking water distribution networks is presented. Statistical analysis and Bayesian network (BN) modelling were used to evaluate relationships among water quality parameters in distribution pipes and their dependencies on weather parameters. Two robust predictive models for Total Bacteria in the network were built based on a deep learning approach (Long Short-Term Memory (LSTM)). The first model included water quality parameters alone as inputs while the second model included weather parameters. The seven-year dataset used in this study constituted water quality parameters measured at seven location in the water distribution network for the city of Ålesund in Norway, and weather data for the same period. Results of the initial statistical analysis and the BN models showed that, air temperature, the summer season, precipitation, as well as water quality parameters namely, residual chlorine, water temperature, alkalinity and electrical conductivity have strong relations with the counts of Total Bacteria in the distribution networks studied. It was found that the integration of the weather parameters in the Total Bacteria prediction models significantly improved the quality of the predictions. Compared to the LSTM 1, LSTM 2 achieved MAE and MSE values as high as to 6.8 and 4.9 times respectively when the model was tested on the seven locations. In addition, the R² values were marginally higher in LSTM 2 (0.92-0.95) than in LSTM (0.81-0.86). The prediction results demonstrate the relevance of integrating weather parameters such as air temperature seasons in predicting bacteria levels in water distribution systems. This suggests that changes in the microbial quality of water in distribution systems and potentially drinking water sources could be reliably assessed by integrating online sensors of water quality and weather parameters with efficient models such as the LSTM. Applying this efficient modelling approach in the management of water supply systems could offer immense support in addressing current challenges in assessing the microbial quality of water and minimizing associated health risks.

The relationship between environmental parameters and microbial water quality at two Costa Rican beaches from 2002 to 2017

Environmental conditions influence fecal indicator bacteria (FIB) levels, which are routinely used to characterize recreational water quality. This study examined 15 years of environmental and FIB data at Puntarenas and Jacó beach, Costa Rica. FIB relationships with sea level, wave height, precipitation, direct normal irradiance (DNI), wind, and turbidity were analyzed. Pearson's correlations identified lags between 24 and 96 h among environmental parameters and FIB. Multiple linear regression models composed of environmental parameters explained 24% and 27% of fecal coliforms and enterococci variability in Jacó, respectively. Puntarenas's models explained 17-26% of fecal coliforms and 12-18% enterococci variability. Precipitation, sea level anomalies, and wave height most frequently explained FIB variability. Hypothesis testing often identified significant differences in precipitation, wave height, daily sea level anomalies, and maximum sea level 24 h prior between days with and without FIB threshold exceedance. Unexpected FIB interactions with DNI, sea level, and turbidity highlight the importance of future investigations.

Preliminary Microbiological Coastal Water Quality Determination along the Department of Atlántico (Colombia): Relationships with Beach Characteristics

Beach water quality is an important factor concerning public health and tourism linked to the “Sun, Sea and Sand” market and is usually assessed in international regulations by the quantification of Escherichia coli and enterococci counts. Despite Salmonella spp. detection not being included in international normative, the presence/absence of this bacteria is also an indicator of seawater quality. The objective of this study was to determine microbiological quality of beach water at 14 beaches along the Department of Atlántico (Colombia) and its relationship with beach characteristics as beach typology (i.e., urban, village, rural and remote areas), presence of beach facilities (e.g., bars, restaurants, etc.) and streams outflowing into the coastline. Sampling program aimed to analyse E. coli and Salmonella spp., by culture-based and real time PCR methods, respectively. Microbiological outcomes were compared with beach characteristics, and a cluster analysis was performed. E. coli and Salmonella spp. were detected in 70% and 20% of samples, respectively. Highest E. coli counts were observed at beaches classified as urban and at Sabanilla, a rural beach with presence of numerous beach restaurants/bars. Salmonella spp. presence was associated with streams that lack wastewater treatment systems. Cluster analysis clearly evidenced the relationship between E. coli and Salmonella spp. and beach characteristics, allowing to obtain indications to implement management programs. According to data obtained, monitoring programs have to be especially carried out in urban areas and at places with beach facilities. This could enhance microbiological water quality and consequently, beachgoers safety and touristic beach attractiveness to international visitors.

Examining coastal dynamics and recreational water quality by quantifying multiple sewage specific markers in a North Carolina estuary

Fecal contamination is observed downstream of municipal separate storm sewer systems in coastal North Carolina. While it is well accepted that wet weather contributes to this phenomenon, less is understood about the contribution of the complex hydrology in this low-lying coastal plain. A quantitative microbial assessment was conducted in Beaufort, North Carolina to identify trends and potential sources of fecal contamination in stormwater receiving waters. Fecal indicator concentrations were significantly higher in receiving water downstream of a tidally submerged outfall compared to an outfall that was permanently submerged (p < 0.001), though tidal height was not predictive of human-specific microbial source tracking (MST) marker concentrations at the tidally submerged site. Short-term rainfall (i.e. <12 h) was predictive of E. coli, Enterococcus spp., and human-specific MST marker concentrations (Fecal Bacteroides, BacHum, and HF183) in receiving waters. The strong correlation between 12-hr antecedent rainfall and Enterococcus spp. (r = 0.57, p < 0.001, n = 92) suggests a predictive model could be developed based on rainfall to communicate risk for bathers. Additional molecular marker data indicates that the delivery of fecal sources is complex and highly variable, likely due to the influence of tidal influx (saltwater intrusion from the estuary) into the low-lying stormwater pipes. In particular, elevated MST marker concentrations (up to 2.56 × 10⁴ gene copies HF183/mL) were observed in standing water near surcharging street storm drain. These data are being used to establish a baseline for stormwater dynamics prior to dramatic rainfall in 2018 and to characterize the interaction between complex stormwater dynamics and water quality impairment in coastal NC.

A smartphone-integrated paper sensing system for fluorescent and colorimetric dual-channel detection of foodborne pathogenic bacteria

Infections caused by foodborne microorganisms are a great threat to the global environment and public healthcare today. Thus, rapid, portable and sensitive assays that can realize the identification of foodborne bacteria are highly desired. In this study, a smart fluorescent and colorimetric dual-readout sensing system has been established for simple and rapid E. coli determination by utilizing the Cu²⁺-triggered oxidation of o-phenylenediamine (OPD). Initially, Cu²⁺ can oxidize OPD to OPDox, resulting in an orange-yellow fluorescence and visible pale-yellow color. However, E. coli can effectively reduce Cu²⁺ into Cu⁺, inhibiting the Cu²⁺-triggered oxidation of OPD to OPDox. Consequently, the introduction of E. coli can turn off both the fluorescence and the UV-vis absorbance signals of the OPD-Cu²⁺ system, illustrating an original mechanism for fluorescent and colorimetric dual-channel detection of E. coli. Moreover, a filter paper-based visual sensor was built and coupled with OPD-Cu²⁺ solution under the assistance of a UV lamp. The as-prepared sensor can detect E. coli quantitatively with the help of a typical smartphone color-scanning application (APP). Thus, this study offers a valid dual-mode assay for sensitive and on-site visible detection of E. coli, guaranteeing the reliability of the results and is more attractive for practical use. Graphical Abstract Schematic illustration of the smartphone-integrated sensing system for fluorescent and colorimetric dual-channel detection of E. coli based on the Cu²⁺-OPD system.

Differential decay and prediction of persistence of Enterococcus spp. and Escherichia coli culturable cells and molecular markers in freshwater and seawater environments

To quantify the impact of fecal pollution on the microbiological bathing water quality, predictive modeling is being increasingly used in which the decay rate of the fecal indicators plays an important role. The decay of sewage-sourced enterococci and Escherichia coli culturable cells and their associated molecular markers (16SrRNA) quantified by Quantitative Reverse transcription PCR were measured in controlled microcosms as well in in situ conditions using different water types, from marine waters to fresh waters with intermediate salinity. All bacterial decays were fitted to a first order decay model. In the laboratory study, the light radiation was the most influent factor affecting E. coli and enterococci survival by culture methods although environmental conditions weakly impacted the decay of molecular markers. The results also indicated differential persistence of genetic markers and culturable organisms of fecal indicator bacteria in different water systems. For each bacteria indicator and analytical method, four equations were obtained to predict the time required to have a 90% reduction (T90) according irradiance, salinity and temperature parameters. The weighted model RMSE (Root Mean Square Error) calculated for all field experiments showed that quantification obtained with the equations defined by laboratory-based study compared reasonably well with in-situ observed quantification (0.4 and 0.2 log by standard culture methods for E. coli and Enterococcus spp. and 0.6 and 0.3 log by RT-qPCR for E. coli and Enterococcus spp. respectively). The modeling tool can be used to predict the presence of fecal pollution in marine and fresh waters in combination with either culture based- or rapid molecular methods.

Storm runoff differentially influences the nutrient concentrations and microbial contamination at two distinct beaches in northern China

With the escalating coastal development and loss of vegetated landscape, the volume of storm runoff increases significantly in Chinese coastal cities. To protect human health and valuable recreational resources, it is necessary to develop a quantitative understanding of coastal pollution. Here we studied the influence of storm runoff on the nutrients and microbial pathogens at two popular bathing beaches in northern China. Dongshan Beach, located near the mouth of an urban river, is influenced by non-point source pollution while Tiger-Rock Beach, a coastal beach, is primarily influenced by a point source from a storm drain outfall. Storm runoff significantly (P < 0.001) decreased the salinity and Chl a post-storm at both the beaches, but only reduced the concentration of dissolved inorganic N at Tiger-Rock Beach. Escherichia coli decreased by 68.7% at Dongshan Beach, possibly due to the dilution effect of the stormflow, contradicting the notion of elevated fecal contamination in coastal beaches from storm runoff. Vibrio parahaemolyticus increased at both beaches post-storm, by 155.7% at Dongshan Beach and 136.7% at Tiger-Rock Beach. Regardless of storm impact, both E. coli and V. parahaemolyticus were much higher at Dongshan Beach than that at Tiger-Rock, suggesting the influence of different surrounding topographies. Lastly, the statistical models developed based on the environmental and microbial parameters regression showed predictive power (adjusted R² > 0.5) to estimate the concentration of E. coli at Dongshan Beach and V. parahaemolyticus at Tiger-Rock Beach. Overall, the results suggest the unique role of the individual beaches in attenuating the effect of rainfall on the concentration of microbial pathogens in bathing water quality and provide unique predictive models for recreational water management and public health protection.

Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks

Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

Statistical models of fecal coliform levels in Pacific Northwest estuaries for improved shellfish harvest area closure decision making

There is a substantial need for tools that effectively predict spatial and temporal fecal pollution patterns in estuarine waters. In this study, statistical models of exceedances of shellfish fecal coliform (FC) water quality criteria were developed using a 10-year dataset of FC levels and environmental data. Performance (sensitivity, specificity, and predictive capacity) of five different types of models was tested (MLR regression, Tobit (censored) regression, Firth's binary logistic regression (BLR), classification trees, and mixed-effects regression) for each of three conditionally managed shellfish-harvesting areas in Tillamook Bay, Oregon (USA). The most influential variables were related to precipitation and river stage height in the wet season and wind and tidal-stage in the dry season. Classification tree and Firth's BLR approaches better explained exceedances of shellfish water quality standards than the current closure thresholds. Findings demonstrate the utility of statistical modeling approaches for improved management of shellfish harvesting waters.

Patterns of Host-Associated Fecal Indicators Driven by Hydrology, Precipitation, and Land Use Attributes in Great Lakes Watersheds

Fecal contamination from sewage and agricultural runoff is a pervasive problem in Great Lakes watersheds. Most work examining fecal pollution loads relies on discrete samples of fecal indicators and modeling land use. In this study, we made empirical measurements of human and ruminant-associated fecal indicator bacteria and combined these with hydrological measurements in eight watersheds ranging from predominantly forested to highly urbanized. Flow composited river samples were collected over low-flow ( n = 89) and rainfall or snowmelt runoff events ( n = 130). Approximately 90% of samples had evidence of human fecal pollution, with highest loads from urban watersheds. Ruminant indicators were found in ∼60-100% of runoff-event samples in agricultural watersheds, with concentrations and loads related to cattle density. Rain depth, season, agricultural tile drainage, and human or cattle density explained variability in daily flux of human or ruminant indicators. Mapping host-associated indicator loads to watershed discharge points sheds light on the type, level, and possible health risk from fecal pollution entering the Great Lakes and can inform total maximum daily load implementation and other management practices to target specific fecal pollution sources.

Maxent estimation of aquatic Escherichia coli stream impairment

BACKGROUND

The leading cause of surface water impairment in United States' rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment.

METHODS

We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model's ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters.

RESULTS

Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen.

DISCUSSION

Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic theplethora of existing pathogens in water, incorporating modeling can fine tune an indicator's utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making.

Multiple Linear Regression Models for Predicting Nonpoint-Source Pollutant Discharge from a Highland Agricultural Region

Sediment runoff from dense highland field areas greatly affects the quality of downstream lakes and drinking water sources. In this study, multiple linear regression (MLR) models were built to predict diffuse pollutant discharge using the environmental parameters of a basin. Explanatory variables that influence the sediment and pollutant discharge can be identified with the model, and such research could play an important role in limiting sediment erosion in the dense highland field area. Pollutant load per event, event mean concentration (EMC), and pollutant load per area were estimated from stormwater survey data from the Lake Soyang basin. During the wet season, heavy rains cause large amounts of suspended sediment and the occurrence of such rains is increasing due to climate change. The explanatory variables used in the MLR models are the percentage of fields, subbasin area, and mean slope of subbasin as topographic parameters, and the number of preceding dry days, rainfall intensity, rainfall depth, and rainfall duration as rainfall parameters. In the MLR modeling process, four types of regression equations with and without log transformation of the explanatory and response variables were examined to identify the best performing regression model. The performance of the MLR models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation of the root mean square error (CV(RMSE)), the ratio of the RMSE to the standard deviation of the observed data (RSR) and the Nash–Sutcliffe model efficiency (NSE). The performance of the MLR models of pollutant load except total nitrogen (TN) was good under the condition of RSR, and satisfactory for the NSE and R2. In the EMC and load/area models, the performance for suspended solids (SS) and total phosphorus (TP) was good for the RSR, and satisfactory for the NSE and R2. The standardized coefficients for the models were analyzed to identify the influential explanatory variables in the models. In the final performance evaluation, the results of jackknife validation indicate that the MLR models are robust.

Nucleic acids-based tools for ballast water surveillance, monitoring, and research

Understanding the risks of biological invasion posed by ballast water-whether in the context of compliance testing, routine monitoring, or basic research-is fundamentally an exercise in biodiversity assessment, and as such should take advantage of the best tools available for tackling that problem. The past several decades have seen growing application of genetic methods for the study of biodiversity, driven in large part by dramatic technological advances in nucleic acids analysis. Monitoring approaches based on such methods have the potential to increase dramatically sampling throughput for biodiversity assessments, and to improve on the sensitivity, specificity, and taxonomic accuracy of traditional approaches. The application of targeted detection tools (largely focused on PCR but increasingly incorporating novel probe-based methodologies) has led to a paradigm shift in rare species monitoring, and such tools have already been applied for early detection in the context of ballast water surveillance. Rapid improvements in community profiling approaches based on high throughput sequencing (HTS) could similarly impact broader efforts to catalogue biodiversity present in ballast tanks, and could provide novel opportunities to better understand the risks of biotic exchange posed by ballast water transport-and the effectiveness of attempts to mitigate those risks. These various approaches still face considerable challenges to effective implementation, depending on particular management or research needs. Compliance testing, for instance, remains dependent on accurate quantification of viable target organisms; while tools based on RNA detection show promise in this context, the demands of such testing require considerable additional investment in methods development. In general surveillance and research contexts, both targeted and community-based approaches are still limited by various factors: quantification remains a challenge (especially for taxa in larger size classes), gaps in nucleic acids reference databases are still considerable, uncertainties in taxonomic assignment methods persist, and many applications have not yet matured sufficiently to offer standardized methods capable of meeting rigorous quality assurance standards. Nevertheless, the potential value of these tools, their growing utilization in biodiversity monitoring, and the rapid methodological advances over the past decade all suggest that they should be seriously considered for inclusion in the ballast water surveillance toolkit.

Characterization of suspended bacteria from processing units in an advanced drinking water treatment plant of China

For the drinking water treatment plant (DWTP), the organic pollutant removal was the primary focus, while the suspended bacterial was always neglected. In this study, the suspended bacteria from each processing unit in a DWTP employing an ozone-biological activated carbon process was mainly characterized by using heterotrophic plate counts (HPCs), a flow cytometer, and 454-pyrosequencing methods. The results showed that an adverse changing tendency of HPC and total cell counts was observed in the sand filtration tank (SFT), where the cultivability of suspended bacteria increased to 34%. However, the cultivability level of other units stayed below 3% except for ozone contact tank (OCT, 13.5%) and activated carbon filtration tank (ACFT, 34.39%). It meant that filtration processes promoted the increase in cultivability of suspended bacteria remarkably, which indicated biodegrading capability. In the unit of OCT, microbial diversity indexes declined drastically, and the dominant bacteria were affiliated to Proteobacteria phylum (99.9%) and Betaproteobacteria class (86.3%), which were also the dominant bacteria in the effluent of other units. Besides, the primary genus was Limnohabitans in the effluents of SFT (17.4%) as well as ACFT (25.6%), which was inferred to be the crucial contributors for the biodegradable function in the filtration units. Overall, this paper provided an overview of community composition of each processing units in a DWTP as well as reference for better developing microbial function for drinking water treatment in the future.

Microbial quality of agricultural water in Central Florida

The microbial quality of water that comes into the edible portion of produce is believed to directly relate to the safety of produce, and metrics describing indicator organisms are commonly used to ensure safety. The US FDA Produce Safety Rule (PSR) sets very specific microbiological water quality metrics for agricultural water that contacts the harvestable portion of produce. Validation of these metrics for agricultural water is essential for produce safety. Water samples (500 mL) from six agricultural ponds were collected during the 2012/2013 and 2013/2014 growing seasons (46 and 44 samples respectively, 540 from all ponds). Microbial indicator populations (total coliforms, generic Escherichia coli, and enterococci) were enumerated, environmental variables (temperature, pH, conductivity, redox potential, and turbidity) measured, and pathogen presence evaluated by PCR. Salmonella isolates were serotyped and analyzed by pulsed-field gel electrophoresis. Following rain events, coliforms increased up to 4.2 log MPN/100 mL. Populations of coliforms and enterococci ranged from 2 to 8 and 1 to 5 log MPN/100 mL, respectively. Microbial indicators did not correlate with environmental variables, except pH (P<0.0001). The invA gene (Salmonella) was detected in 26/540 (4.8%) samples, in all ponds and growing seasons, and 14 serotypes detected. Six STEC genes were detected in samples: hly (83.3%), fliC (51.8%), eaeA (17.4%), rfbE (17.4%), stx-I (32.6%), stx-II (9.4%). While all ponds met the PSR requirements, at least one virulence gene from Salmonella (invA-4.8%) or STEC (stx-I-32.6%, stx-II-9.4%) was detected in each pond. Water quality for tested agricultural ponds, below recommended standards, did not guarantee the absence of pathogens. Investigating the relationships among physicochemical attributes, environmental factors, indicator microorganisms, and pathogen presence allows researchers to have a greater understanding of contamination risks from agricultural surface waters in the field.

Spatial and temporal microbial pollution patterns in a tropical estuary during high and low river flow conditions

Spatial and temporal patterns of coastal microbial pollution are not well documented. Our study examined these patterns through measurements of fecal indicator bacteria (FIB), nutrients, and physiochemical parameters in Hilo Bay, Hawai'i, during high and low river flow. >40% of samples tested positive for the human-associated Bacteroides marker, with highest percentages near rivers. Other FIB were also higher near rivers, but only Clostridium perfringens concentrations were related to discharge. During storms, FIB concentrations were three times to an order of magnitude higher, and increased with decreasing salinity and water temperature, and increasing turbidity. These relationships and high spatial resolution data for these parameters were used to create Enterococcus spp. and C. perfringens maps that predicted exceedances with 64% and 95% accuracy, respectively. Mapping microbial pollution patterns and predicting exceedances is a valuable tool that can improve water quality monitoring and aid in visualizing FIB hotspots for management actions.

Assessing the microbial quality of a tropical watershed with an urbanization gradient using traditional and alternate fecal indicators

Urbanization affects the microbial loading into tropical streams, but its impact on water quality varies across watersheds. Rainfall in tropical environments also complicates microbial dynamics due to high seasonal and annual variations. Understanding the dynamics of fecal contamination in tropical surface waters may be further hindered by limitations from the utilization of traditional microbial indicators. We measured traditional (Enterococcus spp. and Escherichia coli), as well as alternate (enterophages and coliphages) indicators of fecal contamination in a tropical watershed in Puerto Rico during a 1-year period, and examined their relationship with rainfall events across an urbanization gradient. Enterococcus spp. and E. coli concentrations were 4 to 5 logs higher in non-urbanized or pristine sites when compared to enterophages and coliphages, suggesting that traditional fecal indicator bacteria may be natural inhabitants of pristine tropical waters. All of the tested indicators were positively correlated with rainfall and urbanization, except in the most urbanized sites, where rainfall may have had a dilution effect. The present study indicates that utilizing novel indicators of microbial water quality may improve the assessment of fecal contamination and pathogen risk for tropical watersheds.

Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR

The limit of detection (LOD) for qPCR-based analyses is not consistently defined or determined in studies on waterborne pathogens. Moreover, the LODs reported often reflect the qPCR assay alone rather than the entire sample process. Our objective was to develop an approach to determine the 95% LOD (lowest concentration at which 95% of positive samples are detected) for the entire process of waterborne pathogen detection. We began by spiking the lowest concentration that was consistently positive at the qPCR step (based on its standard curve) into each procedural step working backwards (i.e., extraction, secondary concentration, primary concentration), which established a concentration that was detectable following losses of the pathogen from processing. Using the fraction of positive replicates (n = 10) at this concentration, we selected and analyzed a second, and then third, concentration. If the fraction of positive replicates equaled 1 or 0 for two concentrations, we selected another. We calculated the LOD using probit analysis. To demonstrate our approach we determined the 95% LOD for Salmonella enterica serovar Typhimurium, adenovirus 41, and vaccine-derived poliovirus Sabin 3, which were 11, 12, and 6 genomic copies (gc) per reaction (rxn), respectively (equivalent to 1.3, 1.5, and 4.0 gc L(-1) assuming the 1500 L tap-water sample volume prescribed in EPA Method 1615). This approach limited the number of analyses required and was amenable to testing multiple genetic targets simultaneously (i.e., spiking a single sample with multiple microorganisms). An LOD determined this way can facilitate study design, guide the number of required technical replicates, aid method evaluation, and inform data interpretation.

Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff

While the urban runoff are increasingly being studied as a source of fecal indicator bacteria (FIB), less is known about the occurrence of FIB in watershed with mixed land use and ongoing land use and land cover (LULC) change. In this study, Escherichia coli (EC) and fecal streptococcus (FS) were monitored from 2012 to 2013 in agricultural, mixed and urban LULC and analyzed according to the most probable number (MPN). Pearson correlation was used to determine the relationship between FIB and environmental parameters (physicochemical and hydrometeorological). Multiple linear regressions (MLR) were used to identify the significant parameters that affect the FIB concentrations and to predict the response of FIB in LULC change. Overall, the FIB concentrations were higher in urban LULC (EC=3.33-7.39; FS=3.30-7.36log10MPN/100mL) possibly because of runoff from commercial market and 100% impervious cover (IC). Also, during early-summer season; this reflects a greater persistence and growth rate of FIB in a warmer environment. During intra-event, however, the FIB concentrations varied according to site condition. Anthropogenic activities and IC influenced the correlation between the FIB concentrations and environmental parameters. Stormwater temperature (TEMP), turbidity, and TSS positively correlated with the FIB concentrations (p>0.01), since IC increased, implying an accumulation of bacterial sources in urban activities. TEMP, BOD5, turbidity, TSS, and antecedent dry days (ADD) were the most significant explanatory variables for FIB as determined in MLR, possibly because they promoted the FIB growth and survival. The model confirmed the FIB concentrations: EC (R(2)=0.71-0.85; NSE=0.72-0.86) and FS (R(2)=0.65-0.83; NSE=0.66-0.84) are predicted to increase due to urbanization. Therefore, these findings will help in stormwater monitoring strategies, designing the best management practice for FIB removal and as input data for stormwater models.

On the track for an efficient detection of Escherichia coli in water: A review on PCR-based methods

Ensuring water safety is an ongoing challenge to public health providers. Assessing the presence of fecal contamination indicators in water is essential to protect public health from diseases caused by waterborne pathogens. For this purpose, the bacteria Escherichia coli has been used as the most reliable indicator of fecal contamination in water. The methods currently in use for monitoring the microbiological safety of water are based on culturing the microorganisms. However, these methods are not the desirable solution to prevent outbreaks as they provide the results with a considerable delay, lacking on specificity and sensitivity. Moreover, viable but non-culturable microorganisms, which may be present as a result of environmental stress or water treatment processes, are not detected by culture-based methods and, thus, may result in false-negative assessments of E. coli in water samples. These limitations may place public health at significant risk, leading to substantial monetary losses in health care and, additionally, in costs related with a reduced productivity in the area affected by the outbreak, and in costs supported by the water quality control departments involved. Molecular methods, particularly polymerase chain reaction-based methods, have been studied as an alternative technology to overcome the current limitations, as they offer the possibility to reduce the assay time, to improve the detection sensitivity and specificity, and to identify multiple targets and pathogens, including new or emerging strains. The variety of techniques and applications available for PCR-based methods has increased considerably and the costs involved have been substantially reduced, which together have contributed to the potential standardization of these techniques. However, they still require further refinement in order to be standardized and applied to the variety of environmental waters and their specific characteristics. The PCR-based methods under development for monitoring the presence of E. coli in water are here discussed. Special emphasis is given to methodologies that avoid pre-enrichment during the water sample preparation process so that the assay time is reduced and the required legislated sensitivity is achieved. The advantages and limitations of these methods are also reviewed, contributing to a more comprehensive overview toward a more conscious research in identifying E. coli in water.

Sunny with a chance of gastroenteritis: predicting swimmer risk at California beaches

Traditional beach management that uses concentrations of cultivatable fecal indicator bacteria (FIB) may lead to delayed notification of unsafe swimming conditions. Predictive, nowcast models of beach water quality may help reduce beach management errors and enhance protection of public health. This study compares performances of five different types of statistical, data-driven predictive models: multiple linear regression model, binary logistic regression model, partial least-squares regression model, artificial neural network, and classification tree, in predicting advisories due to FIB contamination at 25 beaches along the California coastline. Classification tree and the binary logistic regression model with threshold tuning are consistently the best performing model types for California beaches. Beaches with good performing models usually have a rainfall/flow related dominating factor affecting beach water quality, while beaches having a deteriorating water quality trend or low FIB exceedance rates are less likely to have a good performing model. This study identifies circumstances when predictive models are the most effective, and suggests that using predictive models for public notification of unsafe swimming conditions may improve public health protection at California beaches relative to current practices.