For this reason, this research estimated the number of Escherichia coli (E. coli) in surface water and recreational beach water transported from three different sources, including i) stream sediment, ii) human and animal feces, and iii) combined sewage overflow (CSO ) derived from an ocean outlet. SALINITY (PPT) AT N1 BETWEEN JUNE 1 AND JUNE 30 OBTAINED FROM NEAR-FIELD MODEL.87 FIGURE 6.7OBSERVED AND SIMULATED E.
Introduction
Motivation and objective
The aim of this research is to determine the dominant pathways of microbial water pollution. Furthermore, this research explored specific applications in each chapter, including the release of bacteria from streambeds via hyporheic exchange (chapter 3), the impact of land use change on FIB transport (chapter 4), the spatiotemporal variation of antibiotic resistance genes (ARGs) ( chapter 5), and assessment of marine spillover scenarios (chapter 6).
Contents of each chapter
However, there were still unexplored applications, such as the release of bacteria from soil via hyporheic exchange and the influence of land use changes on FIB transport.
Literature review
FIB and ARG sources
- WWTPs
- Human and animal feces
- Manure
- Streambed sediment
- Marine outfall
- Aquaculture farms
In rural Laos, Causse et al. 2015) found that the contribution of ruminants to a microbial contamination for E. The previous study found that domestic animals had a higher concentration of pathogens than wild animals (Cox et al. 2005).
Models for bacteria transport
- Hydrological and hydrodynamic models
- Survival of bacteria
- Overland transport
- Release from streambed
Rain is the most influential factor in increasing the abundance of bacteria in streams by washing pollutants off the ground and transporting them to streams via surface runoff (Celico et al. Where Re is the entrainment coefficient (kg m-2s-1), τ is the bed shear stress (N m-2), τc is the critical shear stress (N m-2), and the expression (τ/τc -1) is referred to as the relative shear stress on the sediment bed.
Hydrological Modeling of Fecal Indicator Bacteria in a Tropical Mountain Catchment 16
Methodology
- Study design
- Study area: the Houay Pano catchment
- Climate data
- Flow rate measurements and water quality monitoring
- The SWAT model setup and input parameters
- Fecal bacteria modeling using SWAT
- Metrics of model performance
Once in the tributary channel, sediment direction in the subbasin is a function of the peak rate adjustment factor (ADJ_PKR). With SUFI 2, we evaluated the performance of the model with the p-factor and the r-factor.
Results and discussion
- Stream discharge calibration
- Sediment release calibration
- Bacteria module calibration with original module
- Bacteria module calibration with the resuspension release only
- Inclusion of resuspension release and regrowth
- Inclusion of resuspension release and hyporheic exchange
The performance of the model at the daily time step was thus assessed as satisfactory during this period according to the guidelines proposed by Moriasi et al. The RMSE value of the model is 656 MPN 100 mL-1 and most of the simulated values were zero. Log-transformed comparison with. e) the original bacterial SWAT module; (f) resuspension release procedures; (g) resuspension release and regrowth procedures; (h) resuspension release procedures and.
This suggests that the model description of resuspension and deposition processes might be insufficient to predict lower E. The RMSE of the latter result did not differ from that of the resuspension-only release method (Table 3.2), although peaks during high flow periods were slightly longer (Figure 3.7 (d)). Due to the overestimation, the RMSE was much larger than the RMSE when only the resuspension release was performed (Table 3.2).
Conclusion
Although the extent of resuspension rates is much greater during high flow than during base flow, the duration of base flow is much longer than the duration of high flow events, implying that the contribution of transient flow in the hyporheic region to FIB dynamics is significant. Many water quality models simulate the release of nutrients such as nitrogen and phosphorus from sediments under anoxic/hypoxic conditions or the influence of groundwater on surface water quality. However, none of the current water quality models consider groundwater when simulating FIB dynamics in surface waters.
Here, we emphasize the need to include groundwater when modeling FIB concentrations in a tropical watershed. The improvements to model performance presented here should increase the effectiveness of the model as a decision-making tool and thus aid the development of management options aimed at improving microbiological water quality in tropical catchments.
Modeling the impact of land use change on basin-scale transfer of fecal indicator
- Background
- Methodology
- Study area: the Houay Pano watershed
- Monitoring data
- The SWAT model setup
- Calibration procedure
- Land use change analysis
- Results and Discussion
- Calibration results
- Impacts of land use change under observed weather conditions
- Impacts of land use change under identical weather conditions
- Limitations and suggestions for improvement of SWAT
- Conclusion
The first analysis examined the influence of land use changes on the hydrological cycle and water quality under observed weather conditions. To this end, Ghaffari et al. 2010) simulated three different land use scenarios under the same weather conditions. Similarly, we also analyzed three land use scenarios under the same weather conditions from 2011 to 2013.
We found that the multi-year composite land use map improved the simulation in terms of runoff and suspended solids loading (the corresponding Nash-Sutcliffe efficiency (NSE) increased by 8% and 24%, respectively). This study helped identify watershed-scale physical processes that simulate the impact of land use changes on bacteria under observed and identical weather conditions. We expect that these findings will help improve the understanding of the influence of land use changes on bacterial transport.
Influence of Combined Sewage Overflow (CSO) on Microbial Risk at a Korean Coastal
Methodology
- Study site
- Sampling and storm description
- MPN analysis to E. coli
- Real-time PCR analysis to ARGs
- Statistical analysis
The western region of the beach has a combined sewer system, while the eastern region is currently being changed from a combined to a separated sewer system. Sewage outlets existed on both sides of the beach in 2017, but the location of the western sewage outlet was moved away from the coast in 2018 as shown in Figure 5.1. We used a reaction mixture (20 μL) consisting of 10 μL KAPA SYBR® FAST qPCR Kits (Kapa Biosystems, USA), 0.4 μL each of the forward and reverse primers, 8.2 μL PCR-grade water and 1 μL DNA template.
Detailed information of the primers, probes and constructed standard curves for ARGs are provided in the previous research. Therefore, the relationships between variables can be evaluated by comparing SOM maps of the variables (Kalteh et al. Pearson's correlation is the most commonly used correlation test that measures the linear relationship between paired variables, while Spearman's correlation is a statistical measure for the monotonic relationship is.of the variables.
Results and Discussion
- Weekly variation
- Sub-daily variation of E. coli (3-hour interval)
- Sub-daily variation of E. coli (5-hour interval)
- Influence of environmental variables on ARGs
These pollutants probably originate from several sewage outfalls along the river (Figure 5.1). coli decreased in G1, G4 and G5, probably due to the dilution effect of the rising tide level. ARGs in PC2 increased at low tide (14:00 29 June), indicating the contribution of CSO to the prevalence of ARGs (Figure 5.6). Sul1, blaTEM and aac(6')-lb-cr were the most abundant among ARGs in PC2.
Ratio of mean copies of pooled ARGs (i.e., ARGs in PC1 and ARGs in PC2), E. High concentrations of ARGs in PC1 were associated with low low-rainfall ebb levels in group III. Comparing the SOM maps in Figure 5.7 and the monitoring data from Figure 5.6, we can hypothesize that the abundance of ARGs in PC1 in cluster III was affected by the delayed CSO after rainfall.
Conclusion
We can conclude that bacteria carrying ARGs in PC2 were mainly transported through immediate CSOs (group IV), while other bacteria having ARGs in PC1 existing farther from the beach were released by delayed CSOs (group III). The highest concentrations of mean ARGs in PC1 and PC2 were about 100-fold compared to their baseline concentrations in group III and IV, respectively (Figure 5.7).
Assessment of marine outfall scenarios for reducing microbial pollution on a
Background
However, previous studies have focused on the fate and transport of faecal bacteria in coastal areas, but have rarely been applied to the transport of ARGs. 2015) compared the impact of marine outlets of different extents on E. coli numbers at recreational beaches using hydrodynamic models. However, the impact of marine outlets at different depths has not been reported in previous research.
The main objectives of this study are 1) to predict the transport of E. coli and ARGs originating from marine runoff on the recreational beach, using 3D hydrodynamic modeling, and 2) to assess the different scenarios of management practice ( i.e. different size and depth of marine discharge) with the aim of assessing the impact of sewage on the concentrations of E.
Methodology
- Study site
- E. coli and ARGs measurements
- Numerical modeling
- Outfall extension scenarios
For this reason, the far-field model was used to generate boundary conditions for the near-field model. When considering model stabilization, the time step for the far-field model was set to 1 second. The calibration period for the far-field model was from 1 May – 30 June after 20 warm-up days.
The same far-field model bathymetry data (MOF 2019) were used, and the near-field model bathymetry ranges from 0 to -9.1 m. Similar to the far-field model, the SWAN model was used to generate waves in the near-field model. The near-field model was calibrated against hourly tidal level, water temperature and salinity at one calibration point (N1) (Figure 6.1 (a)) using the same parameter in the far-field model, while E.
Results and Discussion
- Calibration of tide, water temperature and salinity
- E. coli transport simulation
- ARGs transport prediction
- Outfall extension results
Observed and simulated concentrations of ARG1 and ARG2 are shown in Figure 6.9 (d – e) with corresponding precipitation (Figure 6.9 (c)). Among the surface layer outlet extension scenarios (Figure 6.11 (a)), S1, S3 and S5, located near the Minrak land area, transported more E. For two ARG groups, Figure 6.12 illustrates the spatial distribution of depth-averaged ARG1- concentrations derived from surface and bottom layers at 15:00 on 29 June 2018, when the highest ARG1 concentration was monitored.
All extended outlets at sea level showed the movement of ARG1 toward the beach due to the onshore current at that time (Figure 6.12 (a)). The discharge on the seabed resulted in lower ARG1 concentrations at the beach than in the discharge at the sea surface (Figure 6.12 (b)). The spatial distribution of ARG2 on both the sea surface and the bottom (Figure 6.13) was identical to the distribution of E.
Conclusion
Conclusion
Population structure and persistence of Escherichia coli in ditch sediments and water in the Seven Mile Creek Watershed. Sensitivity of streamflow and microbial water quality to future changes in climate and land use in the west of Ireland. Risk of gastrointestinal illness associated with exposure to pathogens in the waters of the Lower Passaic.
Effects of land use on fecal indicator bacteria in the water and soil of a tropical watershed. The occurrence of two genotypes of the tetracycline (TC) resistance gene tet(M) in the TC-resistant bacteria in marine sediments of Japan. Impact of climate change on sediment yield in the Mekong River Basin: a case study of Nam Ou Basin, Lao PDR.
