Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

Extended spectrum beta-lactamase-producing Escherichia coli surveillance in the human, food chain, and environment sectors: Tricycle project (pilot) in Indonesia

The World Health Organization (WHO) has been implementing antimicrobial surveillance with a "One Health" approach, known as the Global Surveillance ESBL E. coli Tricycle Project. We describe the implementation of the Tricycle Project (pilot) in Indonesia, focusing on its results, challenges and recommendations. The samples were 116 patients with bloodstream infections caused by ESBL E. coli, 100 rectal swabs collected from pregnant women, 240 cecums of broiler, and 119 environmental samples, using the standardized method according to the guidelines. ESBL-producing E. coli was found in 40 (40%) of the 100 pregnant women, while the proportion of ESBL-producing E. coli was 57.7% among the total E. coli-induced bloodstream infections. ESBL-producing E. coli was isolated from 161 (67.1%) out of 240 broilers. On the other hand, the average concentration of E. coli in the water samples was 2.0 × 108 CFU/100 mL, and the ratio of ESBL-producing E. coli was 12.8% of total E. coli. Unfortunately, 56.7% of questionnaires for patients were incomplete. The Tricycle Project (pilot) identified that the proportion of ESBL-producing E. coli was very high in all types of samples, and several challenges and obstacles were encountered during the implementation of the study in Indonesia. The finding of this study have implication to health/the antimicrobial resistance (AMR) surveillance. We recommend continuing this project and extending this study to other provinces to determine the AMR burden as the baseline in planning AMR control strategies in Indonesia. We also recommend improving the protocol of this study to minimize obstacles in the field.

Evaluating the impact of hydrometeorological conditions on E. coli concentration in farmed mussels and clams: experience in Central Italy

Highly populated coastal environments receive large quantities of treated and untreated wastewater from human and industrial sources. Bivalve molluscs accumulate and retain contaminants, and their analysis provides evidence of past contamination. Rivers and precipitation are major routes of bacteriological pollution from surface or sub-surface runoff flowing into coastal areas. However, relationships between runoff, precipitation, and bacterial contamination are site-specific and dependent on the physiographical characteristics of each catchment. In this work, we evaluated the influence of precipitation and river discharge on molluscs' Escherichia coli concentrations at three sites in Central Italy, aiming at quantifying how hydrometeorological conditions affect bacteriological contamination of selected bivalve production areas. Rank-order correlation analysis indicated a stronger association between E. coli concentrations and the modelled Pescara River discharge maxima (r = 0.69) than between E. coli concentration and rainfall maxima (r = 0.35). Discharge peaks from the Pescara River caused an increase in E. coli concentration in bivalves in 87% of cases, provided that the runoff peak occurred 1-6 days prior to the sampling date. Precipitation in coastal area was linked to almost 60% of cases of E. coli high concentrations and may enhance bacterial transportation offshore, when associated with a larger-scale weather system, which causes overflow occurrence.

Soft sensor predictor of E. coli concentration based on conventional monitoring parameters for wastewater disinfection control

Real-time acquisition of indicator bacteria concentration at the inlet of disinfection unit is a fundamental support to the control of chemical and ultraviolet wastewater disinfection. Culture-based enumeration methods need time-consuming laboratory analyses, which give results after several hours or days, while newest biosensors rarely provide information about specific strains and outputs are not directly comparable with regulatory limits as a consequence of measurement principles. In this work, a novel soft sensor approach for virtual real-time monitoring of E. coli concentration is proposed. Conventional wastewater physical and chemical indicators (chemical oxygen demand, total nitrogen, nitrate, ammonia, total suspended solids, conductivity, pH, turbidity and absorbance at 254 nm) and flowrate were studied as potential predictors of E. coli concentration relying on data collected from three full-scale wastewater treatment plants. Different methods were compared: (i) linear modeling via ordinary least squares; (ii) ridge regression; (iii) principal component regression and partial least squares; (iv) non-linear modeling through artificial neural networks. Linear soft sensors reached some degree of accuracy, but performances of the artificial neural network based models were by far superior. Sensitivity analysis allowed to prioritize the importance of each predictor and to highlight the site-specific nature of the approach, because of the site-specific nature of relationships between predictors and E. coli concentration. In one case study, pH and conductivity worked as good proxy variables when the occurrence of intense rain events caused sharp increases in E. coli concentration. Differently, in other case studies, chemical oxygen demand, total suspended solids, turbidity and absorbance at 254 nm accounted for the positive correlation between low wastewater quality and E. coli concentration. Moreover, sensitivity analysis of artificial neural network models highlighted the importance of interactions among predictors, contributing to 25 to 30% of the model output variance. This evidence, along with performance results, supported the idea that nonlinear families of models should be preferred in the estimation of E. coli concentration. The artificial neural network based soft sensor deployment for control of peracetic acid disinfectant dosage was simulated over a realistic scenario of wastewater quality recorded by on-line sensors over 2 months. The scenario simulations highlighted the significant benefit of an E. coli soft sensor, which provided up to 57% of disinfectant saving.

Prediction of recreational water safety using Escherichia coli as an indicator: case study of the Passaic and Pompton rivers, New Jersey

As contact with high concentrations of pathogens in a waterbody can cause waterborne diseases, Escherichia coli is commonly used as an indicator of water quality in routine public health monitoring of recreational freshwater ecosystems. However, traditional processes of detection and enumeration of pathogen indicators can be costly and are not time-sensitive enough to alarm recreational users. The predictive models developed to produce real-time predictions also have various methodological challenges, including arbitrary selection of explanatory variables, deterministic statistical approach, and heavy reliance on correlation instead of the more rigorous multivariate regression analyses, among others. The objective of this study is to address these challenges and develop a cost-effective and timely alternative for estimating pathogen indicators using real-time water quality and quantity data. As a case study we use New Jersey, where pathogens represent the most common cause of impairment for water quality, and Passaic and Pompton rivers, which are among the largest in the state and the country. We used Membrane Filtration Method and mColiblue24 media to enumerate Escherichia coli in a total of 69 water samples collected from April to November 2016 from the two rivers. We also collected data on environmental variables concurrently and performed stepwise and logistic regression analyses to address the said methodological challenges and determine the variables significantly predicting whether or not the Escherichia coli count was above prescribed levels for recreation activities. The results show that source water, higher specific conductance, lower pH, and cumulative rainfall for the 72 h antecedent the sampling significantly impacted the density of Escherichia coli. In addition to using the Bagging technique to validate the results, we also assessed Whole Model Tests, R², Entropy R², and Misclassification Rates. This approach improves the prediction of bacteria counts and their use in informing the potential safety/hazard of that waterbody for recreational activities.

Optimum positioning of wastewater treatment plants in a river network: A model-based approach to minimize microbial pollution

Microbial pollution in river networks is widespread, threatening human health and activities. Wastewater treatment plants are a major source of microbial pollution that affects downstream communities. We propose a simple modeling approach to identify possible hot-spots of microbial pollution in river networks receiving treated wastewater. We consider every reach in a river network as a potential site for the disposal of treated wastewater and we identify the corresponding section of the downstream river where the concentration of indicator bacteria exceeds a prescribed threshold value. In this paper, we introduce the methodology and demonstrate its application to a small river basin (Lockwitzbach, Germany). We computed the lengths of the polluted river sections for different scenarios in order to separately identify the impacts of hydrological boundary conditions and bacterial retention processes. Effective parameters describing bacterial retention were inferred from field samples. The proposed modeling approach can be used to generate dynamic maps of safe and vulnerable zones in a river network. Our approach helps disentangle the effects of network structure, hydrological variability and in-stream processes on the location and length of unsafe river sections. Our model can be used to identify optimal sites for the discharge of treated wastewater. For example, in the Lockwitzbach basin, we show that relocating the existing effluent discharge could reduce the stream length affected by severe microbial pollution by almost 30%.

Evaluating temporal controls on greenhouse gas (GHG) fluxes in an Arctic tundra environment: An entropy-based approach

There is significant spatial and temporal variability associated with greenhouse gas (GHG) fluxes in high-latitude Arctic tundra environments. The objectives of this study are to investigate temporal variability in CO₂ and CH₄ fluxes at Barrow, AK and to determine the factors causing this variability using a novel entropy-based classification scheme. In particular, we analyzed which geomorphic, soil, vegetation and climatic properties most explained the variability in GHG fluxes (opaque chamber measurements) during the growing season over three successive years. Results indicate that multi-year variability in CO₂ fluxes was primarily associated with soil temperature variability as well as vegetation dynamics during the early and late growing season. Temporal variability in CH₄ fluxes was primarily associated with changes in vegetation during the growing season and its interactions with primary controls like seasonal thaw. Polygonal ground features, which are common to Arctic regions, also demonstrated significant multi-year variability in GHG fluxes. Our results can be used to prioritize field sampling strategies, with an emphasis on measurements collected at locations and times that explain the most variability in GHG fluxes. For example, we found that sampling primary environmental controls at the centers of high centered polygons in the month of September (when freeze-back period begins) can provide significant constraints on GHG flux variability - a requirement for accurately predicting future changes to GHG fluxes. Overall, entropy results document the impact of changing environmental conditions (e.g., warming, growing season length) on GHG fluxes, thus providing clues concerning the manner in which ecosystem properties may be shifted regionally in a future climate.

Analysis and Comparison of Spatial-Temporal Entropy Variability of Tehran City Microclimate Based on Climate Change Scenarios

Urban microclimate patterns can play a great role for the allocation and management of cooling and heating energy sources, urban design and architecture, and urban heat island control. Therefore, the present study intends to investigate the variability of spatial and temporal entropy of the Effective Temperature index (ET) for the two basic periods (1971–2010) and the future (2011–2050) in Tehran to determine how the variability degree of the entropy values of the abovementioned bioclimatic would be, based on global warming and future climate change. ArcGIS software and geostatistical methods were used to show the Spatial and Temporal variations of the microclimate pattern in Tehran. However, due to global warming the temperature difference between the different areas of the study has declined, which is believed to reduce the abnormalities and more orderly between the data spatially and over time. It is observed that the lowest values of the Shannon entropy occurred in the last two decades, from 2030 to 2040, and the other in 2040–2050. Because, based on global warming, dominant areas have increased temperature, and the difference in temperature is reduced daily and the temperature difference between the zones of different areas is lower. The results of this study show a decrease in the coefficient of the Shannon entropy of effective temperature for future decades in Tehran. This can be due to the reduction of temperature differences between different regions. However, based on the urban-climate perspective, there is no positive view of this process. Because reducing the urban temperature difference means reducing the local pressure difference as well as reducing local winds. This is a factor that can effective, though limited, in the movement of stagnant urban air and reduction of thermal budget and thermal stress of the city.

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.

Explaining and modeling the concentration and loading of Escherichia coli in a stream-A case study

Escherichia coli (E. coli) level in streams is a public health indicator. Therefore, being able to explain why E. coli levels are sometimes high and sometimes low is important. Using citizen science data from Fall Creek in central NY we found that complementarily using principal component analysis (PCA) and partial least squares (PLS) regression provided insights into the drivers of E. coli and a mechanism for predicting E. coli levels, respectively. We found that stormwater, temperature/season and shallow subsurface flow are the three dominant processes driving the fate and transport of E. coli. PLS regression modeling provided very good predictions under stormwater conditions (R² = 0.85 for log (E. coli concentration) and R² = 0.90 for log (E. coli loading)); predictions under baseflow conditions were less robust. But, in our case, both E. coli concentration and E. coli loading were significantly higher under stormwater condition, so it is probably more important to predict high-flow E. coli hazards than low-flow conditions. Besides previously reported good indicators of in-stream E. coli level, nitrate-/nitrite-nitrogen and soluble reactive phosphorus were also found to be good indicators of in-stream E. coli levels. These findings suggest management practices to reduce E. coli concentrations and loads in-streams and, eventually, reduce the risk of waterborne disease outbreak.

Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment

An 11year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52km²) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significant predictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing "point sources" of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential "hot spots" of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding public health. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible "hot spots" of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.

Nutrient loading impacts on culturable E. coli and other heterotrophic bacteria fate in simulated stream mesocosms

Understanding fecal indicator bacteria persistence in aquatic environments is important when making management decisions to improve instream water quality. Routinely, bacteria fate and transport models that rely on published kinetic decay constants are used to inform such decision making but may not adequately represent instream conditions. The objective of this work was to evaluate bacterial responses to applied nutrient amendments and provide additional information regarding bacterial response to applied changes that can be incorporated into future modeling efforts. Re-created stream mesocosms were established in laboratory-based, repurposed algae raceways filled with water and sediment from a small, 3rd order Southeast Texas stream. Mesocosm treatments consisted of low (10x) or high (50x) nutrient doses above ambient water concentrations operated at low (0.032 m/s) or high (0.141 m/s) flow rates. Escherichia coli and heterotrophic bacterial concentrations were quantified in water and sediment over 22 days. No significant differences in kinetic constants were observed among E. coli in water or sediment, and only E. coli in sediment showed any growth response. Heterotrophic plate counts revealed a pronounced growth response in water and sediment within 24 h of nutrient addition but did not differ significantly from control mesocosms. Significant kinetic constant differences between E. coli and heterotrophic bacteria in water were identified (p < 0.01) but did not differ significantly in sediment (p > 0.48). Results indicate that nutrient addition does affect microbial numbers instream, but competition from heterotrophic bacteria may prevent an E. coli growth response.