Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio

Targeting a free viral fraction enhances the early alert potential of wastewater surveillance for SARS-CoV-2: a methods comparison spanning the transition between delta and omicron variants in a large urban center

Introduction

Wastewater surveillance has proven to be a valuable approach to monitoring the spread of SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19). Recognizing the benefits of wastewater surveillance as a tool to support public health in tracking SARS-CoV-2 and other respiratory pathogens, numerous wastewater virus sampling and concentration methods have been tested for appropriate applications as well as their significance for actionability by public health practices.

Methods

Here, we present a 34-week long wastewater surveillance study that covers nearly 4 million residents of the Detroit (MI, United States) metropolitan area. Three primary concentration methods were compared with respect to recovery of SARS-CoV-2 from wastewater: Virus Adsorption-Elution (VIRADEL), polyethylene glycol precipitation (PEG), and polysulfone (PES) filtration. Wastewater viral concentrations were normalized using various parameters (flow rate, population, total suspended solids) to account for variations in flow. Three analytical approaches were implemented to compare wastewater viral concentrations across the three primary concentration methods to COVID-19 clinical data for both normalized and non-normalized data: Pearson and Spearman correlations, Dynamic Time Warping (DTW), and Time Lagged Cross Correlation (TLCC) and peak synchrony.

Results

It was found that VIRADEL, which captures free and suspended virus from supernatant wastewater, was a leading indicator of COVID-19 cases within the region, whereas PEG and PES filtration, which target particle-associated virus, each lagged behind the early alert potential of VIRADEL. PEG and PES methods may potentially capture previously shed and accumulated SARS-CoV-2 resuspended from sediments in the interceptors.

Discussion

These results indicate that the VIRADEL method can be used to enhance the early-warning potential of wastewater surveillance applications although drawbacks include the need to process large volumes of wastewater to concentrate sufficiently free and suspended virus for detection. While lagging the VIRADEL method for early-alert potential, both PEG and PES filtration can be used for routine COVID-19 wastewater monitoring since they allow a large number of samples to be processed concurrently while being more cost-effective and with rapid turn-around yielding results same day as collection.

Use of acetaminophen and sucralose as co-analytes to differentiate sources of human excreta in surface waters

Reducing pathogenic risks in surface waters impacted by leaking or overflowing sewage requires the ability to detect human excreta in raw sewage, discriminate human excreta from other types of animal excreta, and differentiate between treated wastewater and raw sewage. We used the relative concentrations of a degradable, human-specific pharmaceutical and a persistent artificial sweetener to indicate the presence of human excreta, its degree of environmental degradation, and the amount of dilution by freshwater sources. Samples were collected and analyzed for acetaminophen and sucralose between 2016 and 2018 from wastewater treatment plants (WWTPs) and streams in metropolitan Lexington, Kentucky (USA). Both co-analytes were consistently present in raw sewage, with acetaminophen in higher concentrations than sucralose. The presence of acetaminophen was related primarily to untreated human excreta, with concentrations rapidly decreasing upon treatment to nearly undetectable levels in WWTP effluents and streams. Sucralose in surface waters was related to inputs of both raw sewage and WWTP effluents. The ratio of acetaminophen to sucralose concentrations in raw sewage and spiked river waters exhibited linear decay kinetics with respect to time, with larger decay constants observed at higher temperatures. This co-analyte indicator approach was evaluated at a local site previously suspected of receiving raw sewage. The presence and ratios of the co-analytes indicated the presence of domestic sewage that was not fully treated.

Relationships between chemical and microbial faecal source tracking markers in urban river water and sediments during and post-discharge of human sewage

This study explores the relationships between faecal source tracking (FST) markers (quantitative Polymerase Chain Reaction (qPCR) markers and steroids), microbial indicators, the faecal ageing ratio of atypical colonies/total coliforms (AC/TC) and potential human pathogens (Giardia, Cryptosporidium and Campylobacter). Faecal source PCR markers tested were GenBac3, HumM3, HumBac (HF183-Bac708R); Bifidobacterium adolescentis, wildfowl and canine-associated markers. Sediment and water samples from the Avon River were collected during and post-discharge of untreated human sewage inputs, following a series of earthquakes, which severely damaged the Christchurch sewerage system. Significant, positive Spearman Ranks (r_s) correlations were observed between human-associated qPCR markers and steroid FST markers and Escherichia coli and F-specific RNA bacteriophage (r_s 0.57 to 0.84, p < 0.001) in water samples. These human source indicative FST markers demonstrated that they were also effective predictors of potentially pathogenic protozoa in water (r_s 0.43-0.74, p ≤ 0.002), but correlated less well with Campylobacter. Human-associated qPCR and steroid markers showed significant, substantial agreement between the two FST methods (Cohen's kappa, 0.78, p = 0.023), suggesting that water managers could be confident in the results using either method under these contamination conditions. Low levels of fluorescent whitening agents (FWA) (mean 0.06 μg/L, range 0.01-0.40 μg/L) were observed in water throughout the study, but steroids and FWA appeared to be retained in river sediments, months after continuous sewage discharges had ceased. No relationship was observed between chemical FST markers in sediments and the overlying water, and few correlations in sediment between chemical FST markers and target microorganisms. The low values observed for the faecal ageing ratio, AC/TC in water, were significantly, negatively correlated with increasing pathogen detection. This study provides support for the use of the AC/TC ratio, and qPCR and steroid FST markers as indicators of health risks associated with the discharge of raw human sewage into a freshwater system.

Fecal source tracking methods to elucidate critical sources of pathogens and contaminant microbial transport through New Zealand agricultural watersheds - A review

In New Zealand, there is substantial potential for microbial contaminants from agricultural fecal sources to be transported into waterways. The flow and transport pathways for fecal contaminants vary at a range of scales and is dependent on chemical, physical and biological attributes of pathways, soils, microorganisms and landscape characteristics. Understanding contaminant transport pathways from catchment to stream can aid water management strategies. It is not practical, however to conduct direct field measurement for all catchments on the fate and transport of fecal pathogens due to constraints on time, personnel, and material resources. To overcome this problem, fecal source tracking can be utilised to link catchment characteristics to fecal signatures identifying critical sources. In this article, we have reviewed approaches to identifying critical sources and pathways for fecal microorganisms from agricultural sources, and make recommendations for the appropriate use of these fecal source tracking (FST) tools.

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.

Canonical Variable Selection for Ecological Modeling of Fecal Indicators

More than 270,000 km of rivers and streams are impaired due to fecal pathogens, creating an economic and public health burden. Fecal indicator organisms such as are used to determine if surface waters are pathogen impaired, but they fail to identify human health risks, provide source information, or have unique fate and transport processes. Statistical and machine learning models can be used to overcome some of these weaknesses, including identifying ecological mechanisms influencing fecal pollution. In this study, canonical correlation analysis (CCorA) was performed to select parameters for the machine learning model, Maxent, to identify how chemical and microbial parameters can predict impairment and F-somatic bacteriophage detections. Models were validated using a bootstrapping cross-validation. Three suites of models were developed; initial models using all parameters, models using parameters identified in CCorA, and optimized models after further sensitivity analysis. Canonical correlation analysis reduced the number of parameters needed to achieve the same degree of accuracy in the initial model (84.7%), and sensitivity analysis improved accuracy to 86.1%. Bacteriophage model accuracies were 79.2, 70.8, and 69.4% for the initial, CCorA, and optimized models, respectively; this suggests complex ecological interactions of bacteriophages are not captured by CCorA. Results indicate distinct ecological drivers of impairment depending on the fecal indicator organism used. impairment is driven by increased hardness and microbial activity, whereas bacteriophage detection is inhibited by high levels of coliforms in sediment. Both indicators were influenced by organic pollution and phosphorus limitation.

Estimation of concentration ratio of indicator to pathogen-related gene in environmental water based on left-censored data

A stochastic model for estimating the ratio between a fecal indicator and a pathogen based on left-censored data, which includes a substantially high number of non-detects, was constructed. River water samples were taken for 16 months at six points in a river watershed, and conventional fecal indicators (total coliforms and general Escherichia coli), genetic markers (Bacteroides spp.), and virulence genes (eaeA of enteropathogenic E. coli and ciaB of Campylobacter jejuni) were quantified. The quantification of general E. coli failed to predict the presence of the virulence gene from enteropathogenic E. coli, different from what happened with genetic markers (Total Bac and Human Bac). A Bayesian model that was adapted to left-censored data with a varying analytical quantification limit was applied to the quantitative data, and the posterior predictive distributions of the concentration ratio were predicted. When the sample size was 144, simulations conducted in this study suggested that 39 detects were enough to accurately estimate the distribution of the concentration ratio, when combined with a dataset with a positive rate higher than 99%. To evaluate the level of accuracy in the estimation, it is desirable to perform a simulation using an artificially generated left-censored dataset that has the identical number of non-detects as the actual data.

Phenotypic antibiotic resistance of Escherichia coli and E. coli O157 isolated from water, sediment and biofilms in an agricultural watershed in British Columbia

This study examined the distribution of antibiotic resistant Escherichia coli and E. coli O157 isolated from water, sediment and biofilms in an intensive agricultural watershed (Elk Creek, British Columbia) between 2005 and 2007. It also examined physical and chemical water parameters associated with antibiotic resistance. Broth microdilution techniques were used to determine minimum inhibitory concentrations (MIC) for E. coli (n=214) and E. coli O157 (n=27) recovered isolates for ampicillin, cefotaxime, ciprofloxacin, nalidixic acid, streptomycin and tetracycline. Both E. coli and E. coli O157 isolates showed highest frequency of resistance to tetracycline, ampicillin, streptomycin and nalidixic acid; respectively. For E. coli, the highest frequency of resistance was observed at the most agriculturally-impacted site, while the lowest frequency of resistance was found at the headwaters. Sediment and river rock biofilms were the most likely to be associated with resistant E. coli, while water was the least likely. While seasonality (wet versus dry) had no relationship with resistance frequency, length of biofilm colonization of the substratum in the aquatic environment only affected resistance frequency to nalidixic acid and tetracycline. Multivariate logistic regressions showed that water depth, nutrient concentrations, temperature, dissolved oxygen and salinity had statistically significant associations with frequency of E. coli resistance to nalidixic acid, streptomycin, ampicillin and tetracycline. The results indicate that antibiotic resistant E. coli and E. coli O157 were prevalent in an agricultural stream. Since E. coli is adept at horizontal gene transfer and prevalent in biofilms and sediment, where ample opportunities for genetic exchange with potential environmental pathogens present themselves, resistant isolates may present a risk to ecosystem, wildlife and public health.

Characterization of coastal urban watershed bacterial communities leads to alternative community-based indicators

Background

Microbial communities in aquatic environments are spatially and temporally dynamic due to environmental fluctuations and varied external input sources. A large percentage of the urban watersheds in the United States are affected by fecal pollution, including human pathogens, thus warranting comprehensive monitoring.

Methodology/Principal Findings

Using a high-density microarray (PhyloChip), we examined water column bacterial community DNA extracted from two connecting urban watersheds, elucidating variable and stable bacterial subpopulations over a 3-day period and community composition profiles that were distinct to fecal and non-fecal sources. Two approaches were used for indication of fecal influence. The first approach utilized similarity of 503 operational taxonomic units (OTUs) common to all fecal samples analyzed in this study with the watershed samples as an index of fecal pollution. A majority of the 503 OTUs were found in the phyla Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria. The second approach incorporated relative richness of 4 bacterial classes (Bacilli, Bacteroidetes, Clostridia and α-proteobacteria) found to have the highest variance in fecal and non-fecal samples. The ratio of these 4 classes (BBC∶A) from the watershed samples demonstrated a trend where bacterial communities from gut and sewage sources had higher ratios than from sources not impacted by fecal material. This trend was also observed in the 124 bacterial communities from previously published and unpublished sequencing or PhyloChip- analyzed studies.

Conclusions/Significance

This study provided a detailed characterization of bacterial community variability during dry weather across a 3-day period in two urban watersheds. The comparative analysis of watershed community composition resulted in alternative community-based indicators that could be useful for assessing ecosystem health.

Determination of fecal contamination indicator sterols in an Australian water supply system

This paper reports a reconnaissance survey of the concentrations of sterol compounds (as indicators of fecal contamination) in a large water supply system in southeast Australia comprising a network of rivers, channels, and drains. Levels of coprostanol and cholestanol were determined in surface water and bottom sediment using gas chromatography-mass spectrometry analysis across 17 strategic sampling sites and over 12 months. Clear differences in the levels of fecal contamination were observed among sites. Four sites routinely contained high levels of the fecal indicator sterols indicated from surface water and sediment sample analysis. Coprostanol concentrations at each location varied from 0 ng/L at the reference site to 11,327 ng/L in a surface water sample of a drain directly downstream of a knackery. The majority of the sites contained coprostanol in the range of 500 to 800 ng/L. Since no fecal-associated sterol compounds were detected at the external reference sites, these were assumed to be free from fecal contamination. Sewage water discharge and/or substantial water runoff maybe the principal factors contributing to fecal contamination of the supply drains and channels.