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

Colloidal iron species driven enhanced H2O2 decomposition into hydroxyl radicals for efficient removal of methylene blue from water

Colloids are wide-spread in natural waters and colloid-facilitated transport via adsorption was established as the most important mechanism for the mobilization of aqueous contaminants. This study reports another possible, but reasonable, role of colloids for the contaminants driven by redox reactions. Under the same conditions (pH 6.0, 0.3 ml 30% H₂O₂, and 25 °C), the degradation efficiencies of methylene blue (MB) at 240 min over Fe colloid, Fe ion, Fe oxide and Fe(OH)₃ were 95.38%, 42.66%, 4.42% and 9.40%. We suggested that, Fe colloid can promote the H₂O₂ based in-situ chemical oxidation process (ISCO) compared with other iron species such as Fe(Ⅲ) ion, Fe oxide and Fe(OH)₃ in natural water. Furthermore, the MB removal via adsorption by Fe colloid was only 1.74% at 240 min. Hence, the occurrence, behavior and fate of MB in Fe colloid containing natural water system mainly depends on the reduction-oxidation rather than adsorption-desorption process. Based on the mass balance of colloidal iron species and characterization of iron configurations distribution, Fe oligomers were the active and dominant components for Fe colloid-driven enhanced H₂O₂ activation among three types of Fe species. The quick and steady conversion of Fe(III) to Fe(II) was proven to be reason why Fe colloid can efficiently react with H₂O₂ to produce hydroxyl radicals.

Removal of Fecal Indicator Bacteria by River Networks

Fecal contamination is a significant source of water quality impairment globally. Aquatic ecosystems can provide an important ecosystem service of fecal contamination removal. Understanding the processes that regulate the removal of fecal contamination among river networks across flow conditions is critical. We applied a river network model, the Framework for Aquatic Modeling in the Earth System (FrAMES-Ecoli), to quantify removal of fecal indicator bacteria by river networks across flow conditions during summers in a series of New England watersheds of different characteristics. FrAMES-Ecoli simulates sources, transport, and riverine removal of Escherichia coli (E. coli). Aquatic E. coli removal was simulated in both the water column and the hyporheic zone, and is a function of hydraulic conditions, flow exchange rates with the hyporheic zone, and die-off in each compartment. We found that, at the river network scale during summers, removal by river networks can be high (19–99%) with variability controlled by hydrologic conditions, watershed size, and distribution of sources in the watershed. Hydrology controls much of the variability, with 68–99% of network scale inputs removed under base flow conditions and 19–85% removed during storm events. Removal by the water column alone could not explain the observed pattern in E. coli, suggesting that processes such as hyporheic removal must be considered. These results suggest that river network removal of fecal indicator bacteria should be taken into consideration in managing fecal contamination at critical downstream receiving waters.

Evaluation of E. coli in sediment for assessing irrigation water quality using machine learning

Fresh produce irrigated with contaminated water poses a substantial risk to human health. This study evaluated the impact of incorporating sediment information on improving the performance of machine learning models to quantify E. coli level in irrigation water. Field samples were collected from irrigation canals in the Southwest U.S., for which meteorological, chemical, and physical water quality variables as well as three additional flow and sediment properties: the concentration of E. coli in sediment, sediment median size, and bed shear stress. Water quality was classified based on E. coli concentration exceeding two standard levels: 1 E. coli and 126 E. coli colony forming units (CFU) per 100 ml of irrigation water. Two series of features, including (FIS) and excluding (FES) sediment features, were selected using multi-variant filter feature selection. The correlation analysis revealed the inclusion of sediment features improves the correlation with the target standards for E. coli compared to the models excluding these features. Support vector machine, logistic regression, and ridge classifier were tested in this study. The support vector machine model performed the best for both targeted standards. Besides, incorporating sediment features improved all models' performance. Therefore, the concentration of E. coli in sediment and bed shear stress are major factors influencing E. coli concentration in irrigation water.

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.

Factors influencing the persistence of enteropathogenic bacteria in wetland habitats and implications for water quality

AIMS

To better understand the persistence dynamics of enteropathogenic bacteria in freshwater wetland habitats, we constructed lab-scale mesocosms planted with two different wetland plant species using a subsurface flow wetland design. Mesocosms were treated with either a high-quality or a poor-quality water source to examine the effects of water quality exposure and plant species on Escherichia coli, Salmonella spp. and Enterococcus spp. in the rhizoplane, rhizosphere and water of wetland habitats.

METHODS AND RESULTS

Quantities of study micro-organisms were detected using real-time PCR in wetland mesocosms. A combination of molecular and culture-based methods was also used to enumerate these organisms from surface water and plant material at high, medium and poor water quality sites in the field. We found that all three enteropathogenic micro-organisms were influenced by microhabitat type and plant species. Organisms differed with respect to their predominant microhabitat and the extent of persistence associated with wetland plant species in the mesocosm study. Of the monitored pathogens, only E. coli was influenced by both water quality treatment and plant species. Salmonella spp. quantities in the rhizoplane consistently increased in all treatments over the course of the mesocosm experiment.

CONCLUSIONS

Plant species selection appears to be an overlooked aspect of constructed wetland design with respect to the removal of enteropathogenic micro-organisms. Escherichia coli and Enterococcus concentrations in wetland outflow were significantly different between the two plant species tested, with Enterococcus concentrations being significantly higher in mesocosms planted with Phalaris arundinaceae and E. coli concentrations being higher in mesocosms planted with Veronica anagallis-aquatica. Furthermore, there is evidence that the rhizoplane is a significant reservoir for Salmonella spp. within wetland habitats.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is the first time that Salmonella spp. has been shown to proliferate under natural conditions within the rhizoplane. This will contribute to our understanding of wetland removal mechanisms for enteropathogenic bacteria. This study identifies the rhizoplane as a potentially important reservoir for human pathogenic micro-organisms and warrants additional study to establish whether this finding is applicable in non-wetland habitats.

Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds

Anticipated future hydroclimatic changes are expected to alter the transport and survival of fecally-sourced waterborne pathogens, presenting an increased risk of recreational water quality impairments. Managing future risk requires an understanding of interactions between fecal sources, hydroclimatic conditions and best management practices (BMPs) at spatial scales relevant to decision makers. In this study we used the Hydrologic Simulation Program FORTRAN to quantify potential fecal coliform (FC - an indicator of the potential presence of pathogens) responses to a range of mid-century climate scenarios and assess different BMP scenarios (based on reduction factors) for reducing the risk of water quality impairment in two, small agricultural watersheds - the Chippewa watershed in Minnesota, and the Tye watershed in Virginia. In each watershed, simulations show a wide range of FC responses, driven largely by variability in projected future precipitation. Wetter future conditions, which drive more transport from non-point sources (e.g. manure application, livestock grazing), show increases in FC loads. Loads typically decrease under drier futures; however, higher mean FC concentrations and more recreational water quality criteria exceedances occur, likely caused by reduced flow during low-flow periods. Median changes across the ensemble generally show increases in FC load. BMPs that focus on key fecal sources (e.g., runoff from pasture, livestock defecation in streams) within a watershed can mitigate the effects of hydroclimatic change on FC loads. However, more extensive BMP implementation or improved BMP efficiency (i.e., higher FC reductions) may be needed to fully offset increases in FC load and meet water quality goals, such as total maximum daily loads and recreational water quality standards. Strategies for managing climate risk should be flexible and to the extent possible include resilient BMPs that function as designed under a range of future conditions.

Citizen science in hydrological monitoring and ecosystem services management: State of the art and future prospects

Hydrological monitoring is essential to guide evidence-based decision making necessary for sustainable water resource management and governance. Limited hydrometric datasets and the pressure on long-term hydrological monitoring networks make it paramount to explore alternative methods for data collection. This is particularly the case for low-income countries, where data scarcity is more pronounced, and where conventional monitoring methods are expensive and logistically challenging. Citizen science in hydrological research has recently gained popularity and crowdsourced monitoring is a promising cost-effective approach for data collection. Citizen science also has the potential to enhance knowledge co-creation and science-based evidence that underpins the governance and management of water resources. This paper provides a comprehensive review on citizen science and crowdsourced data collection within the context of hydrology, based on a synthesis of 71 articles from 2001 to 2018. Application of citizen science in hydrology is increasing in number and breadth, generating a plethora of scientific data. Citizen science approaches differ in scale, scope and degree of citizen involvement. Most of the programs are found in North America and Europe. Participation mostly comprises a contributory citizen science model, which engages citizens in data collection. In order to leverage the full potential of citizen science in knowledge co-generation, future citizen science projects in hydrology could benefit from more co-created types of projects that establish strong ties between research and public engagement, thereby enhancing the long-term sustainability of monitoring networks.

Do reductions in agricultural field drainage during the growing season impact bacterial densities and loads in small tile-fed watersheds?

Predicting bacterial levels in watersheds in response to agricultural beneficial management practices (BMPs) requires understanding the germane processes at both the watershed and field scale. Controlling subsurface tile drainage (CTD) is a highly effective BMP at reducing nutrient losses from fields, and watersheds when employed en masse, but little work has been conducted on CTD effects on bacterial loads and densities in a watershed context. This study compared fecal indicator bacteria (FIB) [E. coli, Enterococcus, Fecal coliform, Total coliform, Clostridium perfringens] densities and unit area loads (UAL) from a pair of flat tile-drained watersheds (∼250-467 ha catchment areas) during the growing season over a 10-year monitoring period, using a before-after-control-impact (BACI) design (i.e., test CTD watershed vs. reference uncontrolled tile drainage (UCTD) watershed during a pre CTD intervention period and a CTD-intervention period where the test CTD watershed had CTD deployed on over 80% of the fields). With no tile drainage management, upstream tile drainage to ditches comprised ∼90% of total ditch discharge. We also examined FIB loads from a subset of tile drained fields to determine field load contributions to the watershed drainage ditches. Statistical evidence of a CTD effect on FIB UAL in the surface water systems was not strong; however, there was statistical evidence of increased FIB densities [pronounced when E. coli >200 most probable number (MPN) 100 mL^-1] in the test CTD watershed during the CTD-intervention period. This was likely a result of reduced dilution/flushing in the test CTD watershed ditch due to CTD significantly decreasing the amount of tile drainage water entering the surface water system. Tile E. coli load contributions to the ditches were low; for example, during the 6-yr CTD-intervention period they amounted to on average only ∼3 and ∼9% of the ditch loads for the test CTD and reference UCTD watersheds, respectively. This suggests in-stream, or off-field FIB reservoirs and bacteria mobilization drivers, dominated ditch E. coli loads in the watersheds during the growing season. Overall, this study suggested that decision making regarding deployment of CTD en masse in tile-fed watersheds should consider drainage practice effects on bacterial densities and loads, as well as CTD's documented capacity to boost crop yields and reduce seasonal nutrient pollution.