Riverbank filtration is an established and quantitatively important approach to mine high-quality raw water for 
drinking water production. Bacterial fecal indicators are routinely used to monitor hygienic raw water quality, 
however, their applicability in viral contamination has been questioned repeatedly. Additionally, there are 
concerns that the increasing frequency and intensity of meteorological and hydrological events, i.e., heavy 
precipitation and droughts leading to high and low river levels, may impair riverbank filtration performance. In 
this study, we explored the removal of adenovirus compared with several commonly used bacterial and viral 
water quality indicators during different river levels. In a seasonal study, water from the Rhine River, a series of 
groundwater monitoring wells, and a production well were regularly collected and analyzed for adenovirus, 
coliphages, E. coli, C. perfringens, coliform bacteria, the total number of prokaryotic cells (TCC), and the number 
of virus-like particles (TVPC) using molecular and cultivation-based assays. Additionally, basic physico-chemical 
parameters, including temperature, pH, dissolved organic carbon, and nutrients, were measured. The highest 
log10 reduction during the >72 m of riverbank filtration from the river channel to the production well was 
observed for coliforms (>3.7 log10), followed by E. coli (>3.4 log10), somatic coliphages (>3.1 log10), 
C. perfringens (>2.5 log10), and F+ coliphages (>2.1 log10) at high river levels. Adenovirus decreased by 1.6–3.1 
log units in the first monitoring well (>32 m) and was not detected in further distant wells. The highest removal 
efficiency of adenovirus and most other viral and bacterial fecal indicators was achieved during high river levels, 
which were characterized by increased numbers of pathogens and indicators. During low river levels, coliforms 
and C. perfringens were occasionally present in raw water at the production well. Adenovirus, quantified via 
droplet digital PCR, correlated with E. coli, somatic coliphages, TCC, TVPC, pH, and DOC at high river levels. At 
low river levels, adenoviruses correlated with coliforms, TVPC, pH, and water travel time. We conclude that 
although standard fecal indicators are insufficient for assessing hygienic raw water quality, a combination of 
E. coli, coliforms and somatic coliphages can assess riverbank filtration performance in adenovirus removal. 
Furthermore, effects of extreme hydrological events should be studied on an event-to-event basis at high spatial 
and temporal resolutions. Finally, there is an urgent need for a lower limit of detection for pathogenic viruses in 
natural waters. Preconcentration of viral particles from larger water volumes (>100 L) constitutes a promising 
strategy.

How to cite: Wang, H., Knabe, D., Engelhardt, I., Droste, B., Rohns, H.-P., Stumpp, C., Ho, J., and Griebler, C.: Dynamics of pathogens and fecal indicators during riverbank filtration in times of high and low river levels, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4739, https://doi.org/10.5194/egusphere-egu22-4739, 2022.

The main objective of this study was to quantify the distribution of pathogens and antibiotic resistance genes in the vadose zone of the soil aquifer treatment (SAT) system. Soil samples were collected from a treated wastewater infiltration basin to a depth of 25 m in two sampling events: (i) at the end of flooding and infiltration and (ii) following three days of drying before the subsequent flooding event. Viable count parallelly of bacteria compared with microscopic live/dead count and enzymatic activity FDA hydrolysis. The abundance of the total bacteria, coliform, antibiotic resistance bacteria (ARB), were examined.  In addition, total genomic DNA was extracted from soil samples (n-28 for both flooding and drying cycle), and quantitative PCR (qPCR) was used to determine the relative abundance of antimicrobial resistance genes (ARGs), including 1 integron-integrase intI1, blaTEM, blaCTX-M-32, sul1, qnrS.

In both sampling events, the results demonstrate that the distribution of antibiotic resistance genes in the vadose zone exhibits a similar pattern to the one obtained for the examined pathogen. We observed a high concentration of pathogens in topsoil layers and a gradual decline with depth. In this presentation, the profile obtained will be described and discussed with pathogens and ARGs transport and retention in the SAT system. 

How to cite: Yadav, N., Ronen, Z., and Arye, G.: Distribution of pathogens and antibiotic resistance genes in the vadose zone of soil-aquifer treatment (SAT) system., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2616, https://doi.org/10.5194/egusphere-egu22-2616, 2022.

Chromium (VI) is a known toxin and carcinogen which is still abundantly used in various industries primarily as an anti-corrosive agent. Thus in case of accidental spillage of it, without proper treatment and disposal, it might leach into the ground and pollute the soil and the groundwater table. The reactivity and solubility of Cr(VI) is extremely high in water making it more dangerous if consumed. The transport and fate of a contaminant in sub-surface porous media is governed by the processes of advection, dispersion and sorption. The transport of Cr (VI) is highly influenced by the processes of adsorption and desorption. The soil sediments have different physical and chemical properties which affect their adsorption efficiencies to a large extent. Hence, the knowledge of adsorptive capacities of the soil sediments is necessary to determine the time of travel of the contaminant plume in the porous media. The present study is conducted to determine the adsorption efficiency of natural soil if Cr(VI) gets accidentally leaked from a stainless steel manufacturing plant located in Rupnagar district of Punjab, India. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) were performed to assess the surface morphology and chemical composition of the soil layers located above the local water table. The initial concentration of Cr(VI) was taken to be 2 mg/l to conduct the batch adsorption studies. The optimum values of parameters like: dose of soil, change of pH of the solution, the time of contact between the adsorbate and the adsorbent and concentration of metal ion adsorbed, are determined in the study. Langmuir, Freundlich and BET adsorption isotherms along with kinetic models were also examined to investigate the mechanisms of adsorption.

Keywords: Chromium (VI); Adsorption; Natural attenuation; Batch adsorption studies; Adsorption isotherms; Kinetic models.

How to cite: Ganguly, S. and Ganguly, S.: Adsorption of Chromium (VI) by soil sediments in heterogeneous porous media: a case study in Rupnagar district of Punjab, India., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2323, https://doi.org/10.5194/egusphere-egu22-2323, 2022.

Insufficient information is available about the transport of fragmented microplastics in groundwater systems. To understand the transport processes, lab-scale column experiments were performed using fragmented microplastics. To mimic realistic microplastics present in the environment, polystyrene (PS) microspheres of diameter 3 and 10 µm were crushed dynamically into fragmented microplastics for injection. We examined the impacts of key physiochemical factors like concentration, soil grain size, flow velocity, ionic strength and straining. The detection and quantification of fragmented microplastics was carried out using solid-phase cytometry (SPC). We observed a high breakthrough of microplastics in coarse soils, and in fine soils, a lesser breakthrough of microplastics was observed because of straining phenomena and wedging of microplastics. Other influencing factors were: (i) greater flow velocity caused detachment and resulted in low attachment efficiency and (ii) ionic strength was found to have a smaller impact on microplastics transport due to their strong negative charge. Results and conclusions from the study provide a baseline of valuable information in order to better understand the mobility of small-sized fragmented microplastics through the soil-aquifer system.

How to cite: Ameen, A., Stevenson, M. E., Jakwerth, S., and Blaschke, A. P.: Transport of dynamically fragmented polystyrene (PS) microplastics through saturated porous media, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6608, https://doi.org/10.5194/egusphere-egu22-6608, 2022.

In this work, we investigate the interplay between dose-response models and permeability heterogeneity on the resilience of an aquifer contaminated with emerging contaminants under uncertainty. We focus our attention to Bisphenol A (BPA) in groundwater which is known to cause endocrine-related effects on humans. Health risks are computed through two distinct BPA dose-response models. The first one is a non-monotonic dose-response (NMDR) model while the second one is a monotonic dose-response (MDR) model. Through the use of a Monte Carlo numerical framework, we simulate transport of BPA from a source to an environmentally sensitive target in a three-dimensional aquifer. Results indicate the importance of considering both hydrological and toxicological information in water resources management. The magnitude and the uncertainty associated with the resilience loss are strongly impacted by the functional shape of the dose-response model and the level of heterogeneity. Further analysis indicates that the role of the ratio of the volumetric flow rate passing through the source zone to the ambient groundwater flow rate in controlling the aquifer resilience loss.

How to cite: Im, J., Rizzo, C., and de Barros, F.: Analysis of the joint impact of dose-response models and permeability heterogeneity on aquifer resilience loss due to Bisphenol A contamination, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6668, https://doi.org/10.5194/egusphere-egu22-6668, 2022.

Interfacial transport across the free surface flow and obstructed region is critical for understanding the scalar transport and mixing in physical situations such as proximity of open water with vegetation in the aquatic system, sediment-water interface (SWI) in river and estuaries, tree canopies in the atmospheric boundary layer, mixing in coral reef and biofilm formation over biological systems. This interaction occurs over a wide range of Spatio-temporal scales due to fast and slow flow in the free layer and porous media which is determined by the key parameters such as degree of flow unsteadiness and porosity. In these situations, understanding and predicting the spreading of the scalar is crucial for water quality assessment and the health of aquatic ecosystems. In this study, we conduct a high-resolution numerical simulation of an array of circular cylinders packed with channels at moderate Reynolds number (Re). As the Reynolds number increases gradually, we observe that particle tends to form coherent structures at the interface as well as filamentation of tracer behind the cylinders. It is worthwhile to note that filaments are a good candidate for mixing as they enhance concentration gradient which is easily erased by molecular diffusion. Breakthrough curves (BTCs) are measured at the midpoint and outlet of the domain to investigate the spreading of tracers using a random walk-based particle tracking method. We found that as the Re decreases, BTCs become broader because the tracer spends a longer time near cylinder boundaries and within the coherent structure before exiting the domain. These BTCs are successfully predicted by the continuous-time random walk model.

How to cite: Bairwa, A., Shukla, M., Khosa, R., and Maheswaran, R.: Impact of Reynolds number on tracer spreading in porous media, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5622, https://doi.org/10.5194/egusphere-egu22-5622, 2022.

The microbial quality of irrigation water is an important factor in the field of food safety. Concentrations of the microbial contamination indicator, Escherichia coli (E. coli), are used to make microbial water quality determinations. However, relationships between the concentrations of E. coli and water quality parameters are often non-linear. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to estimate E. coli concentrations in agricultural pond waters with several popular ML algorithms and observe the differences in model performances. Two ponds in mid-Atlantic U. S. were monitored biweekly during the irrigation seasons of 2016, 2017, and 2018. Samples were collected across the two ponds and E. coli concentrations were measured concurrently with 12 other water quality parameters. The resulting datasets were used to estimate E. coli concentrations using stochastic gradient boosting machines, random forest, support vector machines, and k-nearest neighbor algorithms. The performance of the algorithms was compared by treating performance metrics as statistics obtained by Monte-Carlo modeling of the algorithms. The results of repeated 10-fold cross-validation showed that the random forest model provided the lowest RMSE value for predicted E. coli concentrations in both ponds for individual years and when multi-year datasets were evaluated. However, in most cases there was no significant difference (P > 0.05) between RMSE of random forest and other ML models. For individual years, the normalized RMSE of the predicted E. coli concentrations (log₁₀ CFU 100 mL^-1) ranged from 0.071 to 0.124 and from 0.102 to 0.155 for Ponds 1 and 2, respectively. For the 3-year datasets, these values were 0.119 and 0.132 for Ponds 1 and 2, respectively.  Turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature were the most important predictors as identified by a recursive feature elimination analysis. Model predictive performance did not significantly differ when the 5 least expensive and time-consuming predictors were used as compared with the complete set of predictors. Machine learning appeared to be efficient in discerning complex relationships between E. coli and water quality parameters which describe the aquatic habitat.  

How to cite: Stocker, M., Pachepsky, Y., and Hill, R.: Estimating E. coli concentrations in irrigation pond waters with machine learning algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1833, https://doi.org/10.5194/egusphere-egu22-1833, 2022.

Microbial quality of irrigation water is a public health concern. Management decisions are routinely based on concentrations of fecal indicator bacteria like Escherichia coli (E. coli) in water sources. In the case of irrigation ponds, water samples are often collected near the banks and at shallow depths due to convenient access. However, we hypothesized that water is drawn from locations far from the shoreline and far below the water surface during irrigation events. We used the Environmental Fluid Dynamic Code (EFDC) water quality model to test this hypothesis by simulating water and tracer movement in a working irrigation pond. The initial condition datasets included (1) setting the tracer concentration to zero at the shoreline and to unity in the interior and (2) setting the tracer concentration to zero at the surface water layer and to unity in the deeper layers. Tracer transport to the intake location was simulated with and without wind effect. The simulated concentrations at the intake were close to unity, indicating an insignificant contribution of the near-surface and nearshore layers to the intake concentrations. Four years of biweekly sampling of nearshore and interior locations in two irrigation ponds revealed statistically different average E. coli concentrations between nearshore and interior locations under various environmental conditions. Additionally, when three-dimensional monitoring was conducted, significantly different E. coli concentrations between water depth layers (e.g., 0 m to 0.5 m, 0.5 m to 1 m, 1 m to 1.5 m, etc.) were frequently observed. Mixing water from different depths or locations in the same water body in response to the initiation of irrigation did not homogenize the tracer in the pond. Hydrodynamic modeling showed that the E. coli concentration at the intake changed over the course of the irrigation event in response to the 3D spatial heterogeneity of concentrations measured in the pond water. The results of this work show that 1) water samples should be collected from the pond interior at depths close to the irrigation water intake, and 2) water must be sampled several times during irrigation events if samples are taken at the irrigated fields rather than in ponds.

How to cite: Kim, S., Stocker, M., Sharma, M., and Pachepsky, Y.: Improving microbial quality assessment for irrigation water in ponds using the Environmental Fluid Dynamic Code, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6483, https://doi.org/10.5194/egusphere-egu22-6483, 2022.

The climate-induced increase in precipitation extremes leads to more frequent combined sewer overflows (CSOs) from wastewater treatment plants into urban rivers, which are often used for recreation. This study simultaneously investigates the changes in precipitation extremes, CSO frequency and volume, the resulting fecal microbial loads to streams, and the human infection risks during recreational use.

Our model approach consists of four steps. First, a disaggregation model is used to increase the temporal resolution of the 22 climate scenarios used to cope with the dynamics of urban hydrological processes. Then, continuous simulations are performed using an urban hydrological model (SWMM) for the C20 period (1971-2000), the near-term future (2021-2050), and the long-term future (2071-2100). We simulated the microbial load of the combined sewer discharge with the fecal indicators E. coli, C. perfringens, a human-associated genetic fecal marker HF183/BacR287, and the pathogens Giardia and Cryptosporidium spp. To determine the dilution in the stream, rainfall-runoff modeling is performed using a conceptual semi-distributed hydrological model in the third step for the urban catchment towards the point of CSO discharge. In the final step, a quantitative microbial risk assessment (QMRA) is performed to quantify the potential human infection risks during recreational use.

A hypothetical urban drainage system serves as the study area, which was adapted to the local conditions of a subarea of the city of Vienna including a receiving river. For the precipitation extremes, average increases in precipitation of 13 % for the near future and 19 % for the long-term future are determined over the 22 climate scenarios and 5 rainfall stations considered (extreme event durations 5 min to 24 h, recurrence intervals 0.33 yrs to 10 yrs).

The increase in precipitation extremes results in a higher number of CSOs for both the near- and long-term future. The simulated discharge of the receiving river is often still unaffected by the rainfall event at the time of discharge due to the concentration-time of the catchment, resulting in no direct relationship between discharge and CSO. A realistic estimate of the microbial load discharges during extreme rainfall events is possible for the first time based on the simultaneous continuous hydrological and urban hydrological models in this study.

The resulting concentrations of E. coli, C. perfringens, HF183/BacR287, Giardia, and Cryptosporidium spp. in the receiving water as well as the potential infection risks are analyzed separately on a seasonal and annual basis. For both pathogens, infection risks in the distant future are found to increase in all seasons, with lower increases in the winter months (December-February) than in the rest of the year. The highest risk of infection is found in autumn (September-November).

How to cite: Kilic, H. S., Müller-Thomy, H., Cervero-Arago, S., Linke, R., Lindner, G., Walochnik, J., Sommer, R., Juergen, K., Farnleitner, A., Blaschke, A. P., and Derx, J.: Climate change impact on precipitation extremes and associated infection risks from combined sewer overflows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3762, https://doi.org/10.5194/egusphere-egu22-3762, 2022.

Water quality modelling is essential to integrated water resources management and decision-making, as it improves the understanding of the spatial and temporal dynamics of chemical and microbial pollution in a river system. Understanding of the spatio-temporal dynamics of pollution and accurate prediction of its pollution hotspots are vital to improving the microbial quality of surface water. South African rivers generally receive waste from inadequate wastewater infrastructure, mines, and farming activities, among others. The uMsunduzi River in KwaZulu-Natal, South Africa, is among rivers with recorded poor to very poor water quality. To identify parts of the uMsunduzi River that are polluted by Escherichia coli (E. coli) and Cryptosporidium, chosen to represent bacteria and protozoan parasites respectively, this study mapped out pollutants emanating from point and non-point sources using the Soil and Water Assessment Tool (SWAT) model. SWAT uses a combination of empirical and physically based equations that use readily available inputs and enables users to study long term impacts. Streamflow calibration in the upper and lower reaches of the catchment showed good performance with R² of 0.64 and 0.58, respectively. The SWAT module for predicting microorganism loads and concentrations in the river was used. The main faecal sources in the uMsunduzi catchment can be summarised as: wastewater treatment plant (WWTP), broken sewers in the urban area, and faecal droppings from grazing livestock. The microorganism loads from these sources were described  in SWAT using data from different local water authorities and stakeholders. With respect to E. coli, the output from SWAT was compared to observed data from four points within the catchment representing upper rural, upper urban, lower urban, and lower rural parts. The output from the SWAT model showed slightly low variability, however, the trend in the SWAT model simulations followed the observed data patterns in most subbasins. The trend with Cryptosporidium was such that concentrations are higher downstream the WWTP than upstream, though insufficient data exists to compare the model Cryptosporidium output with observed data. Overall, the model microbial output showed that in rural areas, animals contribute more to pathogen loads than human sources. Human sources were more prominent in urban areas owing to the major contributions from wastewater infrastructure. The microbial output data from the SWAT model were used as input for quantitative microbial risk assessment (QMRA). Considering that not all E. coli are pathogenic, 8% of E. coli was assumed as pathogenic following various studies. The exposure routes investigated were direct ingestion of the uMsunduzi River water during recreational swimming, canoeing training, and drinking.  The exposed population was categorised as children (<18 years old) and adults (>18 years old). The probability of infection for most users exceeds the acceptable level for drinking and recreation as outlined in the South African water quality guidelines and by the World Health Organisation (WHO).

The results of this study can be used as a baseline to assess the economic and health implications of different management plans, resulting in better-informed, cost-effective, and impactful decision-making.

How to cite: Ngubane, Z., Bergion, V., Dzwairo, B., Troell, K., Amoah, I., Stenström, T. A., and Sokolova, E.: Water quality modelling to assess sources and transport of pathogens within uMsunduzi catchment, South Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2157, https://doi.org/10.5194/egusphere-egu22-2157, 2022.

Vembanad Estuary is located in the southern part of India and is also the country's longest lake. Its outlet to the Arabian sea is near Cochin. Particle trapping is one of the main issues found in this estuary. It causes the contaminants to stay in the region for extended durations, which can cause multiple problems. A large number of pollutants enter the Vembanad estuary due to the six rivers that discharge in the Vembanad lake. It is essential to comprehend the movement of these contaminants through the estuary to identify their effect on aquatic ecology and water quality. Flow in the lake is affected by numerous forcing parameters like inflowing rivers, tides, and other boundary conditions. The lack of standard methods to model particulate flow in such a complex environment poses a challenge in understanding flow dynamics and requires identifying new modeling methods.

In the present work, various sections of Vembanad are simulated to identify the trapping zones of the system. Lagrangian simulations of these individual parts of the lake are performed. The simulation results are further analyzed to obtain Lagrangian coherent structures (LCS) using Finite-Time Lyapunov Exponents (FTLE). LCS based on maximum FTLE values shows the dynamic boundaries present in the system, which help identify regions where potential trapping of non-inertial contaminants can occur. Lagrangian particle tracking also aids in recording the total movement of particulate matter from its initial position, which is used to find the resident time of these particles. The result of the study can also be used to find the potential risk posed by the non-inertial contaminants at a location based on their resident times.   

How to cite: Shukla, M., Bairwa, A., and Khosa, R.: A dynamic system theory approach to identify contaminant trapping zones in Vembanad Estuary using Lagrangian Coherent structures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11456, https://doi.org/10.5194/egusphere-egu22-11456, 2022.