In recent years, we have conducted research into developing new pathogen surrogates and synthetic DNA tracers for water applications. Biomolecule-modified particles have been used to mimic Cryptosporidium, rotavirus and adenovirus with respect to their filtration removal and transport in porous media. Additionally, we have developed new DNA tracers as free DNA molecules or DNA-encapsulated biopolymer microparticles to track water contamination. DNA markers are also used to label some surrogates to facilitate their sensitive detection by using qPCR.

The surrogates have been validated in laboratory conditions alongside the actual pathogens. The Cryptosporidium surrogates have been satisfactorily validated in alluvial sand, in limestone sand, in coagulation and rapid sand filtration studies. The rotavirus surrogates have been successfully validated in coastal sand aquifer media, in unmodified and hydrophobically modified quartz sand, and in stony alluvial soils under on-site wastewater applications. The research findings have demonstrated that these new surrogates significantly outperform the most commonly used existing surrogates, namely, unmodified microspheres for Cryptosporidium oocysts and MS2 phage for viruses. Working with the water industry, we have applied the Cryptosporidium surrogate to pilot-scale rapid sand filters and point-of-use domestic filters and determined its removal efficiencies in water filtration systems commonly used in New Zealand. The artificial DNA tracers have been validated in surface water, groundwater and soils, and they were readily trackable in a surface stream for up to 1 km.

Our proof-of-concept studies suggest that the new pathogen surrogates and synthetic DNA tracers we have developed show great promise as new tools for water applications. The ‘micro mimics’ approach has opened up a new avenue for assessing pathogen removal and transport in water systems without the risk and expense that accompany work with actual pathogens. With further validation, the new surrogates could be used to study pathogen removal and transport in subsurface media after the disposal of effluent and biosolids to land, and to assess the performance of filtration processes in water and wastewater treatment. With future up-scaling validation of the new synthetic DNA tracers, these tracers could be useful for concurrently tracking multiple pollution sources and pathways in freshwater environments.

How to cite: Pang, L.: Development of new pathogen surrogates and synthetic DNA tracers for water applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6583, https://doi.org/10.5194/egusphere-egu21-6583, 2021.

Enteric bacterial pathogens in irrigation water can be a public health and food safety issue when contaminating produce. Microbial water quality varies depending on the location and type of water source. We hypothesis temporally stable spatial patterns in levels of Salmonella enterica and Listeria monocytogenes exist in individual water sources in the same region. To test this hypothesis, samples were collected from six water sources in the mid-Atlantic U.S over two years, twice every month during growing season (May to September) and once a month during non-growing seasons (October to April). The sampling sites represented four rivers and two ponds. Several environmental covariates (conductivity, ORP, pH, salinity, dissolved oxygen, turbidity, cumulative rainfall for one and seven day prior to sampling dates) were measured in conjunction with quantitative pathogen recovery. Temporal stability was quantified by computing the mean relative difference (MRD) of pathogen levels from the average across the monitoring location at the same sampling dates. The Spearman rank correlation coefficient between each pathogen level on successive sampling dates was calculated. Levels of both pathogens at each location demonstrated temporally stable spatial patterns. The overall MRD values of pathogens in river water were higher than MRD values of pathogens in pond water. The MRD values for S. enterica were similar to the MRD values of cumulative 7-day rainfall. An inverse relationship between the MRD values of L. monocytogenes and water temperature were found.  The Spearman rank correlation for both pathogens indicated moderate to strong relationship between consecutive sampling events in approximately 80% of cases. These can be used to improve regional monitoring of microbial quality of irrigation water.

How to cite: Kim, S., Pachepsky, Y., White, C., Gerdes, M., Goldstein, R., Sapkota, A., Micallef, S., Kniel, K., Parveen, S., Hashem, F., and Sharma, M.: Temporal stability of Salmonella enterica and Listeria monocytogenes levels across irrigation water types, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6109, https://doi.org/10.5194/egusphere-egu21-6109, 2021.

Phytoplankton is known to affect freshwater habitats of pathogenic and indicator organisms in irrigation water sources. Cyanobacteria are associated with producing harmful toxins which can be transferred to crops, and the gene transfer between phytoplankton and pathogens is of interest particularly in connection with the antibiotic resistance in microorganisms. The objective of this work was to evaluate the possibilities of estimating phytoplankton populations in irrigation ponds by using separate and combined in situ water quality sensing/sampling and sUAS imagery. The study was conducted during a 5-month summer-early fall period at a working irrigation pond on Maryland’s Eastern shore, USA. In situ physical, biochemical, and nutrient measurements were taken at 34 locations in the pond with a total of 21 parameters. Phytoplankton species were enumerated using a modified Ütermohl method and then grouped into green algae, diatoms, and cyanobacteria. The imagery was obtained from an altitude of 120 meters using three modified GoPro cameras and a MicaSense camera. It was then clipped to represent the area around locations of sensing/sampling. The measured parameters were grouped into physical, biochemical, nutrient, and imagery datasets as inputs. Various combinations of these inputs constituted 17 different datasets used with machine learning algorithms. The target variables were the three groups of phytoplankton and the proportion of cyanobacteria in the total count of observed phytoplankton cells. The regression tree (RT) algorithm was applied to research the structure of the dataset and to determine the major influential variables. The random forest (RF) algorithm was applied to estimate the target variables for each of the 15 total datasets. With the RT analysis, nutrient concentrations appear to be influential for green algae and cyanobacteria proportion. After the nutrients were added to the physical and biochemical parameters in the RT analysis for these specific variables, the R² went from 0.782 to 0.869 and from 0.678 to 0.758, respectively. The imagery alone provided moderate RT accuracy for green algae (R²=0.661) and cyanobacteria (R²=0.586), but less for diatoms (R²=0.483). The RT analysis provided good estimates for green algae with the R² of 0.756 but was not efficient for diatoms (avg. R²=0.524), cyanobacteria (avg. R²=0.284), nor the proportion of cyanobacteria (avg. R²=0.524). In the random forest study, the most important predictors for green algae and cyanobacterial proportion were nutrient concentrations of potassium and calcium, respectively. MicaSense imagery at the red edge and near-infrared parts of the spectrum were among the most important predictors. The drone-based imagery provided information useful for the estimation and prediction of green algae. Influential input variables were different amongst phytoplankton groups. For the 17 input datasets, the overall accuracy increased in the sequence imagery < physical < biochemical < nutrient water quality parameters as inputs.  

How to cite: Smith, J. E., Griffith, B. J., Stocker, M. D., Kim, M. S., and Pachepsky, Y. A.: Estimating phytoplankton species populations in irrigation ponds from drone-based imagery and in situ water quality sensing and sampling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6395, https://doi.org/10.5194/egusphere-egu21-6395, 2021.

Alluvial wetlands are important natural habitats and contain valuable drinking water resources. The transport of pathogens via the inflows of river water or the release and runoff from animal faecal deposits into the backwater bodies can pose health risks. The aim of this study was to develop a combined modelling approach for predicting the concentrations and loads of protozoan reference pathogens during floods and rainfall events in an alluvial wetland river. The probabilistic-deterministic model QMRAcatch (v 1.1 python backwater) was newly adapted to account for short-time variations in the flow and microbial transport of alluvial wetlands. The wetland discharge rates, together with the inundated volumes and areas served as input to the model. The latter were determined by means of regression analysis based on results of a 2D hydrodynamic flow model during a flood event. To evaluate the model performance of QMRAcatch, we used concentrations of human, ruminant, pig and bird associated microbial faecal source tracking (MST) markers and E. coli measured in the Danube and in the wetland river from 2010 to 2015. The microbial die-off / degradation was identified to be the most relevant optimization parameter. To obtain this parameter, we conducted a literature survey on the degradation of MST markers in water environments, determined confidence limits of the temperature-dependent rate coefficients, and adjusted them within these limits during the optimization. Scenarios of the different transport pathways of Cryptosporidium and Giardia into the wetland bodies during floods and rainfall events were then simulated. The scenarios showed that the highest loads of Cryptosporidium and Giardia were transported via the main river into the wetland during high flows, followed by the rainfall-induced release from animal faecal deposits, and the resuspension in flooded areas. The combined modelling approach is useful to support the drinking water safety management of alluvial wetlands.

Funding source: This work was supported by the Vienna Science and Technology Fund (WWTF) [grant number ESR17-070] and by the European Union and Vienna Water [programme number LE07-13, project name ‘Groundwater Resource Systems Vienna’].

How to cite: Derx, J., Linke, R., Demeter, K., Komma, J., Cervero-Aragó, S., Schijven, J., Sommer, R., Walochnik, J., Kirschner, A. K. T., Stalder, G., Blaschke, A. P., and Farnleitner, A. H.: Modelling the fate and transport of microbial pathogens during floods and rainfall events in an alluvial wetland area supported by microbial source tracking, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4294, https://doi.org/10.5194/egusphere-egu21-4294, 2021.

Escherichia coli is the most commonly assessed indicator of fecal contamination. The presence of elevated levels of E. coli is used to evaluate the microbial water quality in recreational and irrigation water sources.  E. coli concentrations are spatially and temporally variable. Monitoring of the variability inherent in water measurements can help create and implement effective monitoring designs and solutions. The objective of this work is to determine if there exist spatial patterns that are stable in time over years of observations. Two irrigation ponds in Maryland USA were monitored for three years during the growing seasons (June to August). Water samples and in situ measurements were collected in the same 47 locations biweekly for three years. The presence of stable spatial patterns was researched for relative differences between the logarithm of concentrations in specific locations and the average logarithm across the pond for each of observation times. The mean of these relative differences (MRD) over the observation period formed consistent spatial patterns. We found stable patterns of locations across the pond and found higher MRD values near the banks than the pond interiors.  MRDs computed for separate years were more variable and had amplitudes different from the overall average MRD over the three years, although the similarities between patterns across years was apparent. 

How to cite: Stocker, M., Hill, R., Kim, M., and Pachepsky, Y.: Spatiotemporal variability of E. coli concentrations in two irrigation ponds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6655, https://doi.org/10.5194/egusphere-egu21-6655, 2021.

The intensification of dairy farming on the agricultural landscape in New Zealand has raised concerns about pollution sources from dairy faecal runoff into waterways. The transport of faecal pollution from farms into waterways is facilitated by overland flow, which can result from rain and flood events, poorly designed irrigation practices and the washing down of milking sheds.

An important step for mitigation of pollution is the identification of the source(s) of faecal contamination. When elevated levels of faecal indicator bacteria (FIB) such as Escherichia coli are identified in a waterway, faecal source tracking (FST) tools such as microbial source tracking (MST) using quantitative polymerase chain reaction (qPCR), and faecal steroids (for example, cholesterol) provide information about the sources of faecal contamination. The understanding of the fate (degradation/persistence) and transport of these FST markers in the environment is recognised as an important requirement for the interpretation of water quality monitoring in aquatic environments.

This study investigated the effects of faecal decomposition on bovine faecal indicators (E. coli and FST markers: bovine-associated qPCR markers and ten faecal steroids) by monitoring the effect of flood and rainfall events on simulated cowpats over a five and a half month period under field conditions. Two separate spring/summer trials were conducted to evaluate: Trial 1) the mobilisation under simulated flood conditions of the faecal indicators from irrigated versus non-irrigated cowpats, Trial 2) the mobilisation of faecal indicators from non-irrigated cowpat flood runoff versus runoff after simulated rainfall onto non-irrigated cowpats.

The microbial community changes within the decomposing cowpat (as illustrated by amplicon-based metagenomic analysis) were expected to impact on the survival/persistence of the bacterial targets of the MST markers, and also alter the ratio between faecal sterols and their biodegradation products, the stanols. It was hypothesised, therefore, that there would be:

• Changes over time in the concentration of E. coli and the bovine-associated MST markers mobilised into the cowpat runoff
• Alterations in the FST ratio signature of the ten measured faecal steroids, resulting in a change from a bovine faecal steroid signature in fresh cowpat runoff to other animal faecal signatures in the runoff from decomposing cowpats
• A difference in the mobilisation decline rates of all FST and microbial markers within a treatment regime and between treatments.

Linear regression analysis was undertaken to establish mobilisation decline rates for each of the analytes in the mobilisable phase from the cowpat runoff treatments, with calculation of the time taken in days for reduction in 90% of the concentration (T₉₀), and statistical comparison of the regression coefficients (slopes) of all analytes. The results will include a discussion of the impacts of the study’s observations on the interpretation of faecal indicator assessments for water quality monitoring in waterways influenced by sources of faecal contamination.

How to cite: Devane, M., Gilpin, B., Webster-Brown, J., Weaver, L., Dupont, P., and Wood, D.: Flood and rainfall mobilisation of E. coli and faecal source tracking markers from decomposing cowpats , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3577, https://doi.org/10.5194/egusphere-egu21-3577, 2021.

Rivers transport pathogenic microorganisms (including fecal indicator bacteria and human enteric viruses) from point and non-point sources over long distances, posing a direct risk for human health. Yet, pathogens in surface waters can be deposited and transitorily immobilized and accumulated together with other fine particles in streambed sediments, mostly within the top few centimeters. These dynamic fine particle standing stocks retain and delay downstream transmission of pathogens during baseflow conditions, but contribute to their resuspension and transport downstream during stormflow events. Direct measurements of pathogen accumulation in streambed sediments are rare. Further, it is unknown whether pathogen accumulation is constrained near to the point source inputs or if the continuous deposition and resuspension of pathogens results in the transmission of active pathogens further downstream. 

In this study, we analyze fine particle standing stocks along a 1 km reach of an intermittent Mediterranean stream receiving inputs from the effluent of a wastewater treatment plant (WWTP), during a summer drought when the effluent constituted 100% of the stream flow, and thus, large accumulation and persistence of pathogens along the streambed was expected. We measured abundance of total bacteria, Escherichia coli (as a fecal indicator bacteria), and presence of enteric rotavirus (RoV) and norovirus (NoV). We also monitored environmental variables such as water temperature, dissolved oxygen, total benthic particulate matter, and fraction of organic matter.  Abundance of E. coli, based on qPCR detection, was high  (~ 1 ng/μL) along the first 100 m downstream of the WWTP effluent input, and we found trace amounts of RoV and NoV.  Furthermore, E. coli was present along the first km downstream of the WWTP effluent input with a logarithmic decline in concentration with distance. These results were combined with a particle tracking model that uses stream water velocity as an input and accounts for hyporheic exchange, pathogen immobilization, degradation and resuspension during baseflow and stormflow conditions.  Model results indicate that even at very low flows (<20 L/s), pathogens can be transported over long distances (> 1km), but that the extent of longitudinal transport varies among pathogen types.  These results demonstrate that benthic standing stocks of fine particles act as hot spots of pathogen accumulation in streams, and that the interplay between immobilization, degradation, the extent of resuspension and downstream transport during storms and time between storms determine pathogen concentrations in the streambed. 

How to cite: Drummond, J., Gonçalves, J., Aquino, T., Bernal, S., Gacia, E., Gutierrez-Aguirre, I., Turk, V., Ravnikar, M., Krause, S., and Martí, E.: Pathogen persistence in fine particle standing stocks in an intermittent urban stream, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1301, https://doi.org/10.5194/egusphere-egu21-1301, 2021.

The fate and transport of emerging contaminants in aquatic environments is a complex process, which is not only determined by their own properties but can also be influenced by the surrounding environment. In this study, a comprehensive modelling framework coupling a 3D hydrodynamic--emerging contaminants module was developed to describe the fate and transport of two representative emerging contaminants, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET) in a tropical reservoir. First, the model was calibrated and validated with BPA and DEET obtained from a historical dataset (2013-2014) in bulk water, suspended solids, pore water and sediments phase. Results revealed that the simulation performance gave “excellent simulation” results with skill scores all larger than 0.90. Subsequently, the ecological risk assessment for the reservoir was conducted using the trophic state index (TSI) and coupled species sensitivity distribution (SSD)-Risk Quotient (RQ) method. The RQ values of the study area ranged from 0.003-0.068 (BPA) and 0.001-0.014 (DEET), respectively, which suggests that the levels of studied compounds BPA and DEET may pose low risk to the aquatic ecosystem. Finally, the indirect influence of general water quality parameters such as nutrients (phosphorous) on the multi-compartment distributions of emerging contaminants was explored. Our approach lays down a comprehensive framework to better understand the dynamics of fate and transport and their potential ecological risks of emerging contaminants as well as the indirect impact of other water quality parameters on their distributions in different phases in aquatic ecosystems.

How to cite: Tong, X., Zhang, J., You, L., and Gin, K. Y.-H.: Modeling the fate, transport and risk of Bisphenol A and N, N-diethyltoluamide in a tropical reservoir, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10526, https://doi.org/10.5194/egusphere-egu21-10526, 2021.

Background: Rivers are important sources for drinking water supply, however, they are often impacted by wastewater discharges from wastewater treatment plants (WWTP) and combined sewer overflows (CSO). Reduction of the faecal pollution burden is possible through enhanced wastewater treatment or prevention of CSOs. Few methodological efforts have been made so far to investigate how these measures would affect the long-term treatment requirements for microbiologically safe drinking water supply under future changes.

Objectives: This study aimed to apply a new integrative approach to decipher the interplay between the effects of future changes and wastewater management measures on the required treatment of river water to produce safe drinking water. We investigated scenarios of climate change and population growth, in combination with different wastewater management scenarios (i.e., no upgrades and upgrades at WWTPs, CSOs, and both). To the best of our knowledge, this is the first study to investigate this interplay. We focussed on the viral index pathogens norovirus and enterovirus and made a cross-comparison with a bacterial and a protozoan reference pathogen (Campylobacter and Cryptosporidium).

Methods: We significantly extended QMRAcatch (v1.0 Python), a probabilistic-deterministic model that combines virus fate and transport modelling in the river with quantitative microbial risk assessment (QMRA). To investigate the impact of climatic changes, we used a conceptual semi-distributed hydrological model and regional climate model outputs to simulate river discharges for the period 2035 – 2049. We assumed that population growth leads to a corresponding increase in WWTP discharges. QMRAcatch was successfully calibrated and validated based on a four-year dataset of a human-associated genetic MST marker and enterovirus. The study site was the Danube in Vienna, Austria.

Results: In the reference scenario, approx. 98% of the enterovirus and norovirus loads at the study site (median: 10¹⁰ and 10¹³ N/d) originated from WWTP effluent, while the remainder was via CSO events. The required log reduction value (LRV) to produce safe drinking water was 6.3 and 8.4 log₁₀ for enterovirus and norovirus. Future changes in population size, river flows and CSO events did not affect these treatment requirements, and neither did the prevention of CSOs. In contrast, in the scenario of enhanced wastewater treatment, which showed lower LRVs by 2.0 and 1.3 log₁₀, climate-change-driven increases in CSO events had a considerable impact on the treatment requirements, as they affected the main pollution source. Preventing CSOs and installing enhanced treatment at the WWTPs together had the most significant positive effect with a reduction of LRVs by 3.9 and 3.8 log₁₀ compared to the reference scenario.

Conclusions: The integrative modelling approach was successfully realised. The simultaneous consideration of source apportionment and concentrations of the reference pathogens were found crucial to understand the interplay among the effects of climate change, population growth and pollution control measures. The approach was demonstrated for a study site representing a large river impacted by WWTP and CSO discharges, but is applicable at other sites to support long term water safety planning.

How to cite: Demeter, K., Derx, J., Komma, J., Parajka, J., Schijven, J., Sommer, R., Cervero-Aragó, S., Lindner, G., Zoufal-Hruza, C. M., Linke, R., Savio, D., Ixenmaier, S. K., Kirschner, A. K. T., Kromp, H., Blaschke, A. P., and Farnleitner, A. H.: Microbiological requirements for safe drinking water production at a large river impacted by human wastewater: a scenario analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14863, https://doi.org/10.5194/egusphere-egu21-14863, 2021.

In the region Rastatt/Baden-Baden in the Upper Rhine Valley, Germany, approximately 1000 ha of predominantly agricultural land has been contaminated with per- and polyfluoroalkyl substances (PFASs) about one decade ago when paper-fiber biosolids mixed with compost was applied. These substances affect various land uses (agriculture, open pit gravel quarries, and urban planning) and the underlying aquifer as the main drinking water resource for surrounding cities and municipalities.

Remediation attempts have been limited to date, particularly due to the large spatial extent of the contamination and the related high costs. One strategy currently being investigated is to immobilize the PFASs in the soil in-situ. Substances with a high sorption capacity would be applied on the ground surface and mixed with the soil. The then altered soil should still fulfill its original purpose (e.g., for agriculture). Another strategy could be to remove the contaminated soil and use it for construction (e.g., noise protection embankment) after treatment with the immobilization agents.

The purpose of this research is to develop a test strategy to evaluate the long-term leaching characteristics of soil treated with substances to increase its sorption capacity. Treated soil is tested on three different scales (batch experiments, column experiments, lysimeters) and under different saturation conditions (saturated, variably saturated). Effluent concentrations are monitored over time with different analytical methods (target analysis, determination of sum parameters (EOF/AOF), Total Oxidizable Precursor Assay (TOP)). Mathematical models are employed to evaluate the appropriateness of various processes (e.g., equilibrium sorption) and the leaching behavior for time scales larger than possible in laboratory experiments.

A special challenge for both the analytical strategy and the numerical modeling poses the fact that PFASs consist of a more than 4700 compounds (according to OECD), from which currently only about 20 usually are quantified in routine analysis. A number of these analytical targets are breakdown products, derived from larger precursors by microbial activity, which makes the source term undefined.

The current data illustrate significant reductions in PFAS desorption rates in some of the treated soils. In comparison to the control material (N-1), eluate concentrations in a treated soil (R-1) are found to be lowered by a factor of 1000. The desorbed PFAS mass in the column experiment with R-1 is less than 4%, relative to N-1. In the lysimeter experiments (variably saturated), delayed increasing eluate concentrations indicate additional processes (source term).

The measured and modelled time-series of effluent concentrations serve as the basis for a simple and cost-effective method for the experimental testing of immobilization measures for PFASs.

How to cite: Bierbaum, T., Braun, J., Klaas, N., Haslauer, C., Lange, F. T., Nuerenberg, G., and Scheurer, M.: Testing PFAS Immobilization, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8501, https://doi.org/10.5194/egusphere-egu21-8501, 2021.

Coral sand forms the surficial geology on many coral cay and low-lying atolls, such as are located throughout the Pacific region. Shallow groundwater hosted within such sand is the main source of freshwater for many island communities. It is critically at risk from the impacts of climate-change and anthropogenic stresses. A United Nations' Sustainable Development Goal is to improve water access and sanitation issues in such environs. Working towards that goal, we have conducted a set of laboratory column experiments to obtain some initial measures of microbial removal efficiencies for coral sand substrate from the Pacific atoll of South Tarawa, Kiribati.  

In one experiment we attempted to mimic physio-chemical conditions at the Bonriki Freshwater Reserve that supplies most of the water on South Tarawa. Three small plastic columns were packed with very poorly sorted gravelly coral sand sampled from the reserve. The effective transport of Escherichia coli J6-2 and MS2 bacteriophage through the packed columns was evaluated under saturated flow conditions.

In a second experiment we conducted infiltration tests on naturally well-sorted coral sand, sourced from Bikenibeu beach, South Tarawa. We perceive such sand has potential to be used in the construction of effluent drainage fields from septic tank systems in use on South Tarawa, where currently there are no established design criteria. The sand was packed to a depth of 400 mm in triplicate glass column apparatus. It was conditioned by dosing with septic tank effluent twice per day for 27 days (8 mm head each event). Effluent spiked with bacterial and viral indicator organisms: Escherichia coli J6-2, Enterococci faecalis and MS2 bacteriophage, as well as the viral pathogens: adenovirus, echovirus, norovirus and rotavirus was then dripped on to the columns, as a 35 mm application. Any resulting drainage from the base of the columns was collected and analysed, and the depth profile of the tracer organisms was examined in the sand columns by destructive sampling.

The very poorly sorted coral substrate from Bonriki Reserve proved very effective at attenuating Escherichia coli J6-2 under saturated flow conditions. We estimated a spatial removal rate of 0.05 ± 0.02 log₁₀ cm^-1 for this bacterial tracer. No removal rate could be quantified for the viral indicator. Although overall, our observations suggest the coral sand was significantly less effective at attenuating MS2 bacteriophage than it was at attenuating Escherichia coli J6-2.

In the unsaturated column experiments made on beach sand conditioned with effluent, all the microorganisms examined demonstrated >4-log removal values. Contrary to our finding from the saturated sand column experiment made with material from Bonriki Reserve, the conditioned coral beach sand filters demonstrated higher affinity for MS2 bacteriophage (also viruses) than they did Escherichia coli J6-2, or Enterococci faecalis.

How to cite: Burbery, L. F., Humphries, B., Weaver, L., and Gregor, J.: Microbial removal rate efficiencies measured for coral sand  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16567, https://doi.org/10.5194/egusphere-egu21-16567, 2021.

Upscaling groundwater transport from the column scale to the field scale is relevant because field tests with various tracers are often too expensive or not permissible, due to public health or environmental concerns.  Therefore, when testing chemical or pathogenic tracers, work is often done using small scale columns in the laboratory and results are extrapolated to the field. Several studies compare tracer transport in small-scale columns to tests in the field, but there is yet to be a study that compares groundwater transport using a meso-scale as well. Within a framework of upscaling, three scales are considered: small laboratory columns (0.1 m scale), a large intact core (1 m scale), and a real-world gravel aquifer (10 m scale).  The small column is filled with gravel material taken from boreholes at the field site, which is close to Vienna, Austria.  The meso-scale consists of an undisturbed gravel column, which was taken from a gravel pit near Neuhofen an der Ybbs, Austria. It was found that scale effects observed may be due to heterogeneity at the macropore scale versus preferential flowpaths at the meso-scale and field scale. Additionally, differences may be observed due to the small columns being repacked with aquifer material and the large column and field site being “undisturbed”.  The meso-scale column allows us to gain insight into the upscaling processes by incorporating an in-between step when comparing groundwater transport at the column to the field scale.

How to cite: Stevenson, M., Oudega, T., Lindner, G., Scheidl, A., Eder, A., Strauss, P., and Blaschke, A. P.: Upscaling Subsurface Transport from the Column to the Field: A Focus on the Meso-Scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16095, https://doi.org/10.5194/egusphere-egu21-16095, 2021.

Groundwater contamination and subsequent transport of viruses and bacteria are a major concern in aquifers worldwide. To ascertain the ability of these aquifers to remove pathogens, tracer tests with microbial indicators are carried out. But because these tests are laborious and require special permission, column tests are often done instead. Unfortunately, results from column tests tend to grossly overestimate removal rates λ when compared to the field scale, which can lead to underestimations of groundwater contamination risks. Scale is an important consideration when examining pathogen transport through porous media, as pathogen removal rarely happens by linear processes. Field tests were carried out with Bacillus subtilis endospores and phiX174 coliphages over a distance of 25 m in an alluvial gravel aquifer in Vienna, Austria. The sandy gravel material from the field site was also used in column tests with the same tracers. Both attachment-detachment and Colloid Filtration Theory were used to model these tests. The results show a big difference in removal between the two scales. A comparison with the literature showed a correlation between the heterogeneity (or preferential flow) of the porous media and the difference in removal rates between the column and field scale.

How to cite: Oudega, T., Lindner, G., Derx, J., Farnleitner, A., Sommer, R., Blaschke, A., and Stevenson, M.: Upscaling transport of Bacillus subtilis endospores and phiX174 coliphages in heterogeneous porous media from the column to the field scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2515, https://doi.org/10.5194/egusphere-egu21-2515, 2021.

In the 1950’s, plastics were introduced as a miracle material and since then it has revolutionised human society in almost every domain of our daily life. The benefits of plastics are countless but their inherent resistance to degradation has ultimately led to their accumulation in the environment in the form of micro and nano plastics. In recent years, the presence of microplastics (MP) in fresh water sources has raised questions related to the protection of drinking water. In Austria, the exact status of groundwater contamination by MP is unknown. To understand the behaviour of MP that are present in the environment, a study was conducted to investigate the transport and distribution of MP in groundwater using column experiments.

Polyethylene MP were produced from 3D fluorescent printing material using a milling technique and in a well-defined size range of 1-200 μm. A borosilicate glass column (1.5 cm diameter and 10 cm long) was used as our experimental setup. The columns were packed with quartz and coarse sand. A layer of homogenized MP-sand mixture (approximately 3 to 5% w/w) was applied at the top of a soil column. The transport behaviour of MP were analysed in terms of various physical and chemical factors like MP-concentration, soil particle size, inflow rate, ionic strength and straining effect. The outflow from the column was collected at different pore volume intervals and analysed for the presence of MP. The breakthrough curves (BTCs) were obtained by measuring the MP concentrations of the effluent.

How to cite: Ameen, A., Stevenson, M., and Blaschke, A. P.: Studying the transport and retention of naturally occurring microplastics (MPs) in sandy soils using column experiments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16566, https://doi.org/10.5194/egusphere-egu21-16566, 2021.

Presented work is synthesis of both literature data and own efforts on study of virus transduction and demography models. The aim would be considering of viruses and other sub-cell organisms as needful part of life on Earth basing on fundamental biology and ecology. It is important to understand the negative consequences for humanity and the biosphere of extreme outbreaks of dangerous viruses (Spanish flu, AIDS, etc.). Viruses were discovered by the Russian scientist D. Ivanovsky in 1892 and named "filtering virus". Having the size of a molecule, it passes freely through filters and masks. In the early twentieth century, the Russian scientist V. Vernadsky predicted the existence of a single information field of the biosphere. In 60-s of XX century was opened to the genetic code, which was uniform in all organisms (G. Korana, etc.). 70-ies the phenomenon of "horizontal transfer," i.e., transferability of information among all living organisms on the planet without a sexual process (B. McClintock, M. Golubovsky, etc.) was discovered. Some viruses (e.g. T4) are the most studied organisms on Earth due to its relative simplicity. The number of virus types is not estimated, but can be measured in millions. The number of virus individuals on the planet is estimated at 10³⁹. Viruses are a necessary part of the biosphere. They create a "biological Internet" in which the information unity of organisms is ensured by the constant transfer of DNA sections between all organisms due to vires transduction. Viral epidemics are an example of co-evolution of higher and lower organisms. It temporarily reduces the number of individual species (for example, the number of people during the Spanish flu decreased by 17 million), but never threatens the existence of a particular species. Just as the medical fight against viruses reduces their population, but does not completely destroy them. The human immune system and the virus gene pool are also in a state of co-evolution. The temporary reduction in the number of the host organism of the virus is further compensated by increased immunity and a rise in the birth rate. Viruses activate the immune system of both individuals and humanity as a whole. Man needs them just as small wars are needed to maintain the combat capability of armies. Forecast of negative and positive consequences of virus reproduction is possible basing of modern mathematical ecology and genetics.

How to cite: Sapunov, V.: Role of viruses in biosphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-26, https://doi.org/10.5194/egusphere-egu21-26, 2020.

Recent studies have brought to attention how residual antibiotics from domestic, agricultural and industrial wastes have been found in alarming quantities in the world’s rivers. Evidence is growing that significant concentrations of these substances may lead to the gradual development of drug-resistant bacteria, among many other potential impacts. Still, there is a lack of observational data in the field since these substances are not typically included in routine monitoring programs, especially in developing regions. In this work, we develop a model to estimate the emission of various antibiotics and their subsequent transport in river networks at high spatial resolution and global scale, enabling first-time estimates of the surface-water concentrations of these compounds for virtually any river in the world.

The transport in the river system is estimated using the contaminant fate module in the high-resolution, global river routing model HydroROUT. A key component of this research is the integration of three novel datasets in the modeling approach. These datasets include: (1) the average levels of consumption of antibiotics for each country in the world, which are used to estimate the release of the antibiotics in each region; (2) a global database of wastewater treatment plants (WWTPs); which are used to geo-locate point sources of the contaminant discharges into river networks and (3) a global compilation of measured pharmaceutical concentrations in river reaches that is used for model validation.

The WWTP global database includes detailed information (mostly from official regional or national sources) on 58,502 individual plants such as their facility and discharge locations, population served, flow rate of wastewater discharge, and level of treatment of processed wastewaters. Being essential to spatially explicit water quality assessments, in cases where this information was not available, auxiliary datasets such as a satellite-derived population grid and a DEM-derived river network were used to estimate missing attributes.

The high resolution (500-meters) predictions of the model can be used in a variety of subsequent applications. First, the model can be used to identify specific areas in river networks where high concentrations of contaminants are expected and where field studies should be focused. Secondly, scientists and regulators can use the model to develop screening methods to inform the development of guidelines or regulations designed to minimize the risks associated with the environmental release of pharmaceuticals. Thirdly, governments and operators of wastewater treatment facilities can use the model to set appropriate treatment standards for individual wastewater treatment plants and to ensure that advanced treatment technologies, which are inherently resource-intensive, are deployed only in areas where they are needed. And, finally, wastewater treatment technology providers can use the results to drive the development and deployment of new treatment technologies with potential global markets.

How to cite: Ehalt Macedo, H., Nicell, J., Lehner, B., Khan, U., and Grill, G.: Global assessment of antibiotics in river systems using a high-resolution contaminant fate model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2960, https://doi.org/10.5194/egusphere-egu21-2960, 2021.

Lakes are integrators of environmental and climatic changes occurring within their contributing basins. Understanding the complex behavior of lakes in a changing environment is essential to effective water resource management and mitigation of climate change effects. The ESA CCI Lakes is a multi-disciplinary project (https://climate.esa.int/en/projects/lakes) combining expertise to exploit data to create the largest and longest possible consistent, global record of five lake climate variables: lake water level, extent, temperature, surface-leaving reflectance, and ice cover. The phase 1 version of the database covers 250 globally distributed lakes with temporal coverage, depending on parameter, ranging from 1992 up to 2019. The dataset is planned to expand to 2000 lakes in the second phase. The distribution of the dataset will be introduced over space and time. The potential of the dataset and in particular of data records on chlorophyll-a concentrations, is explored for Lake Trasimeno, a shallow eutrophic lake of central Italy which is a specific case study of the lakes CCI project included in the Long-Term Ecosystem Research (LTER) network. In situ measurements from LTER were used to evaluate satellite products as well as to complete the CCI data record. Meteo-climatic data were extracted to analyze the interrelationships between the trend in water parameters and climate factors. An in situ WISPstation sensor was also used to provide high frequency (every 15 minutes) information on chlorophyll-a and phycocyanin concentration for last two years.
We used Artificial Intelligence (AI) and Non-Parametric Multiplicative Regression (NPMR) techniques to analyze the data. Chlorophyll-a in Lake Trasimeno was dominated by a summer bloom consistently initiating in July and typically peaking in early September and was largely predicted by the time variable - accounting for 87% of feature importance. The North Atlantic Oscillation (NAO) was the next most important variable (4% feature importance) corroborated by NPMR and shown to be largely important during early to mid-September when a positive NAO, associated with high pressure and warm sunny weather, led to an increase in chlorophyll-a concentrations. Regional climatic indices as well as the more obvious nutrient drivers of algal blooms should therefore be considered in lake management. Comparing the high frequency WISPstation data (2018-2020) with the CCI dataset allows for detailed cross validation. Interestingly some of the rapid fluctuations visible from the satellite record that may have been interpreted as noise are supported by the in situ data. In addition, utilizing the phycocyanin results from the WISPstation showed, in near real time, how cyanophytes played a key role in the sudden increases and declines in chlorophyll-a in mid to late summer. Coupling climatic indices, nutrient concentrations and near real time phycocyanin concentrations could be indispensable to the management of blooms in high value recreational lakes such as Trasimeno.

How to cite: Giardino, C., Free, G., Bresciani, M., Pinardi, M., Laanen, M., and Cingolani, A.: The essential climate variables for lakes: exploring satellite products from global to local scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5559, https://doi.org/10.5194/egusphere-egu21-5559, 2021.

Various physical, chemical and biological processes take place three- dimensionally in deep lakes, regulated by complex boundary conditions. Propelled by the rapid development of equipment, technology and computational power, the understanding of deep lakes has steadily advanced. In particular hydrodynamic monitoring and simulation studies have benefitted from combining field observation, numerical simulation and other emerging techniques such as remote sensing. In contrast, water quality parameters are less well investigated by this combination of tools. In this study, we integrate remote sensing techniques with a Lagrangian particle tracking model for lake water quality simulations. Specifically, our goal was to establish a successive individual-based model for health-relevant microorganisms in Lake Geneva. To this end, we combined remote sensing images from the current Sentinel 2 and Sentinel 3 satellites and Delft3D hydrodynamic and particle tracking models. Total suspended matter (TSM), which can both be detected by satellites and simulated by numerical models, is chosen as a parameter of concern. Concentration of TSM in Lake Geneva deduced from remote sensing images is used as observation to compare with particle tracking simulation to support the validation of the numerical model. On the other hand, the model allows to bridge gaps in satellite observations due to cloud coverage. Point source releasing and lake-wide dynamic pattern of TSM are employed as scenario studies to indicate the validation of our particle tracking model, focusing on time spans between 1 to 10 days. Our findings demonstrate that remote sensing images can serve to calibrate and validate the particle tracking water quality model, and in return, the particle tracking model provides the possibilities for data inference and interpolation between satellite images. The flexibility of the Lagrangian particle tracking method poses more possibilities to incorporate flow independent movement, mortality and growth of micro-organisms. It is expected that a more universal and accurate tool for water quality simulation can be created which will facilitate decision making.

How to cite: Li, C., Odermatt, D., Bouffard, D., Wüest, J., and Kohn, T.: Coupling particle tracking model and satellite data for trajectories prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6874, https://doi.org/10.5194/egusphere-egu21-6874, 2021.

Anthropogenic eutrophication is a pressing global environmental problem that threatens the ecological functions of many inland freshwaters and diminishes their abilities to meet their designated uses. Water authorities worldwide are being pressed to manage the negative consequences of harmful algal blooms (HABs) based largely on data collected from conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management. This study assesses the potential of using Sentinel 2 MSI to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly-monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive HABs during the growing season. The performance and transferability of water quality models previously calibrated based on Landsat 7 and 8 surface reflectance to predict Chlorophyll-a (Chl-a), total suspended solids (TSS), Secchi Disk Depth (SDD), and Phycocyanin (PC) levels in the reservoir are first assessed. The results showed poor transferability between Landsat and Sentinel 2, with all models experiencing a significant drop in their predictive skill. Sentinel 2 specific models were then developed for the reservoir based on 153 water quality samples collected over two years. Different model functional forms were then tested, including multiple linear regressions (MR), multivariate adaptive regression splines (MARS), and support vector regressions (SVR). Our results showed that for Chl-a, the MARS model outperformed MR and SVR, with an R² of 60%. Meanwhile, the SVR-based models outperformed their MR and MARS counterparts for TSS, SDD and PC (R² = 59%, 94%, and 72% respectively).

How to cite: Alameddine, I. and Abbas, M.: Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel-2 MSI: the importance of model functional form, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2039, https://doi.org/10.5194/egusphere-egu21-2039, 2021.

Abstract: The traditional water quality monitoring methods are time-consuming and laborious, which can only reflect the water quality status of single point scale, and have some problems such as irregular sampling time and limited sample size. Remote sensing technology provides a new idea for water quality monitoring, and the temporal resolution of MODIS is one day, which is suitable for long-term, continuous real-time large-scale monitoring of lakes. In this study, Lake Simcoe (located in Ontario, Canada) was selected as the research area. The long-term spatiotemporal changes of chlorophyll-a, transparency, total phosphorus and dissolved oxygen were analyzed by comparing the empirical method, multiple linear regression, random forest and neural network with MODIS data. Finally, the water quality condition of Lake Simcoe is evaluated. The results show that the overall retrieval results of two machine learning models are better than that of the empirical method. The optimal retrieval accuracy R² for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13μg/L, 0.3m, 0.002mg/L and 0.14mg/L, respectively. On the annual scale, the annual mean values of the four water quality parameters during the 10-year period from 2009 to 2018 were 1.37μg/L, 6.9m, 0.0112mg/L and 10.17mg/L, respectively. On the monthly scale, chlorophyll a, total phosphorus and dissolved oxygen first decreased and then increased at the time of year. The higher concentrations of chlorophyll a and total phosphorus in the south and east of Lake Simcoe are related to the input of nutrients from the surrounding residents and farmland.

Key words: water quality monitoring; MODIS; empirical method; machine learning

How to cite: Wang, B., Huang, J., and Guo, H.: Long-term water quality monitoring in Lake Simcoe based on the empirical method and machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9027, https://doi.org/10.5194/egusphere-egu21-9027, 2021.

Dissolved oxygen (DO) effectively indicates the health and self-purification capacity of waterbodies. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales are limited. In this study, the support vector regression models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements, water temperature and sampling coordinates of Lake Huron (N = 206) and three other inland waterbodies (N = 282) covering different latitudes. Using the developed models, spatial distributions of the annual and monthly DO since 1984 and the annual monthly DO since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on DO were analyzed. Results showed that the developed models had good robustness and generalization (average R² = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimation by Landsat and MODIS data were highly consistent (average R² = 0.88). Note that the model performance was limited for samples beyond the range of the training set. From 1984 to 2019, the oxygen loss of Lake Huron was 6.56%. The DO of Lake Huron showed obvious seasonal regularity of decreasing from spring to summer and increasing from summer to autumn. Since 2000, DO of Lake Huron has shown a decreasing trend in the same month of different years. Air temperature, incident shortwave radiation flux density and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, adding variables related to the target parameter and extending the training set to cover more water quality conditions could effectively improve model performance.

How to cite: Guo, H., Huang, J., Zhu, X., Wang, B., and Tian, S.: Estimating dissolved oxygen of Lake Huron at multiple spatiotemporal scales using remote sensing and machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1967, https://doi.org/10.5194/egusphere-egu21-1967, 2021.