HS2.3.5

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.

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.

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.

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.

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.

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.