Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment

Eliciting expert judgements to underpin our understanding of faecal indicator organism loss from septic tank systems

Septic tank systems (STS) in rural catchments represent a potential source of microbial pollution to watercourses; however, data concerning the risk of faecal indicator organism (FIO) export from STS to surface waters are scarce. In the absence of empirical data, elicitation of expert judgements can provide an alternative approach to aid understanding of FIO pollution risk from STS. Our study employed a structured elicitation process using the Sheffield Elicitation Framework to obtain expert judgements on the proportion of FIOs likely to be delivered from STS to watercourses, based on 36 scenarios combining: (i) septic tank effluent movement risk, driven by soil hydro-morphological characteristics; (ii) distance of septic tank to watercourse; and (iii) degree of slope. Experts used the tertile method to elicit a range of values representing their beliefs of the proportion of FIOs likely to be delivered to a watercourse for each scenario. The experts judged that 93 % of FIOs would likely be delivered from an STS to a watercourse under the highest risk scenario that combined (i) very high STS effluent movement risk, (ii) STS distance to watercourse <10 m, and (iii) a location on a steep slope with gradient >25 %. Under the lowest risk scenario, the proportion of FIOs reaching a watercourse would likely reduce to 5 %. Expert confidence was high for scenarios that represented extremes of risk, while uncertainty increased for scenarios depicting intermediate risk conditions. The behavioural aggregation process employed to obtain a consensus among the experts proved to be useful for highlighting both areas of strong consensus and high uncertainty. The latter therefore represent priorities for future empirical research to further improve our understanding of potential pollution risk from septic tanks and in turn enable better assessments of potential threats to water quality in rural catchments throughout the world where decentralised wastewater systems are common.

Microbial diversity in dairy manure environment under liquid-solid separation systems

In dairy manure, a wide array of microorganisms, including many pathogens, survive and grow under suitable conditions. This microbial community offers a tremendous opportunity for studying animal health, the transport of microbes into the soil, air, and water, and consequential impacts on public health. The aim of this study was to assess the impacts of manure management practices on the microbial community of manure. The key novelty of this work is to identify the impacts of various stages of manure management on microbes living in dairy manure. In general, the majority of dairy farms in California use a flush system to manage dairy manure, which involves liquid-solid separations. To separate liquid and solid in manure, Multi-stage Alternate Dairy Effluent Management Systems (ADEMS) that use mechanical separation systems (MSS) or weeping wall separation systems (WWSS) are used. Thus, this study was conducted to understand how these manure management systems affect the microbial community. We studied the microbial communities in the WWSS and MSS separation systems, as well as in the four stages of the ADEMS. The 16S rRNA gene from the extracted genomic DNA of dairy manure was amplified using the NovoSeq Illumina next-generation sequencing platform. The sequencing data were used to perform the analysis of similarity (ANOSIM) and multi-response permutation procedure (MRRP) statistical tests, and the results showed that microbial communities among WWSS and MSS were significantly different (p < 0.05). These findings have significant practical implications for the design and implementation of manure management practices in dairy farms.

Insights into the antibacterial activity and antibacterial mechanism of silver modified fullerene towards Staphylococcus aureus by multiple spectrometric examinations

Clarifying the antibacterial mechanism of silver (Ag)-based materials is of great significance for the rational design, synthesis, and evaluation of antimicrobials. Herein, detailed description of the antibacterial mechanism of a synthesized silver deposited fullerene material (Ag(I)-C60) towards Staphylococcus aureus was surveyed from the point of view of DNA damage by ultraviolet-visible spectroscopy (UV-vis), inductively coupled plasma mass spectrometry (ICP-MS), and liquid chromatography-mass spectrometry (LC-MS). The model material, Ag(I)-C60, was prepared by liquid-liquid interfacial precipitation method, and characterized by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), thermos-gravimetric analysis (TGA), and nitrogen adsorption/desorption analysis. Ultra-efficient bacteriostatic rate of Ag(I)-C60 was found to be 88.98% under light irradiation for 20 min. UV-vis measurement of the composition changes of four DNA bases showed that they changed in the presence of Ag(I)-C60 under light irradiation, suggesting Ag(I)-C60 could destroy the cells and genetic material of Staphylococcus aureus and thereby inhibit its growth and reproduction. ICP-MS analysis demonstrated the releasing behavior of Ag+ from Ag-based materials. Finally, the transformation pathway of G, A, C, and T were measured by LC-MS, demonstrating the conversion of Adenine (m/z 136.06) to 8-OH-Ade (m/z 174.04). These collective results suggested that Ag(I)-C60 was a new ultra-efficient antibacterial by slowly releasing Ag+ in water and producing a large amount of ROS under light.

An innovative risk evaluation method on soil pathogens in urban-rural ecosystem

The presence and reproduction of pathogens in soil environment have significant negative impacts on soil security and human health in urban-rural ecosystem. Rapid urbanization has dramatically changed the land use, soil ecosystems, and the presence of pathogens in soil environment, however, the risk associated with soil pathogens remains unknown. Identifying the potential risk of pathogens in soils in urban-rural ecosystem has become an urgent issue. In this study, we established a risk evaluation method for soil pathogens based on analytic hierarchy process and entropy methods to quantitatively estimate the potential risk of soil pathogens to children and adults in urban-rural ecosystem. The abundance and species number of soil pathogens, network structure of soil microbial community, and human exposure factors were considered with 12 indicators to establish the risk evaluation system. The results revealed that 19 potential pathogenic bacteria were detected in soils within a typical urban-rural ecosystem. Substantial differences were observed in both abundance and species of soil pathogens as well as network structure of soil microbial community from urban to rural areas. Urban areas exhibited relatively lower levels of soil pathogenic abundance, but the microbial network was considerably unstable. Rural areas supported relatively higher levels of soil pathogenic abundance and stable microbial networks. Notably, peri-urban areas showed relatively unstable microbial networks alongside higher levels of soil pathogenic abundance compared to other areas. The risk evaluation of soil pathogens for both adults and children showed that peri-urban areas presented the highest potential risk, with children being more susceptible than adults to threats posed by soil pathogens in both urban and peri-urban areas. The established evaluation system provides an innovative approach for quantifying risk of soil pathogens at regional scale and can be used as a reference for preventing soil pathogens contamination and enhancing soil health in areas with intense human activities.

Spatial distribution of bacterial resistance towards antibiotics of rural sanitation system in China and its potential link with diseases incidence

Chinese government is vigorously promoting toilet renovation in rural areas to reduce the risk of human feces exposure, which would cause infectious diseases, especially antibiotic resistance genes (ARGs) and pathogens. However, the distribution of ARGs in human feces from different regions of China remained ill-defined. It is not yet known how the survival of ARGs after toilet treatment is associated with the regional infection rates. Here, we investigated the prevalence of ARGs in human feces in rural areas of China and their potential relationship with infectious diseases for the first large-scale. The results showed that there were still high ARGs residues in human feces after rural toilet treatment, especially tetM-01 and ermB with average relative abundance as high as 1.21 × 10^-1 (Eastern) and 1.56 × 10^-1 (Northern), respectively. At a large regional scale, the significant differences in human feces resistomes were mainly shaped by the toilet types, TN, NH₃-N, and the bacterial community. A critical finding was that toilets still cannot effectively decrease the pathogenicity risk in human feces. The significant positive relationship (P<0.05) between infectious diseases and ARGs can infer that ARGs in human feces exposure might be a critical path for enhancing the incidence of diseases, as these ARGs hinder the effectiveness of antibiotics.

Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA

Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Watershed characteristics, such as urban land, are often associated with impaired microbial water quality. Within the proximity of the New York-New Jersey-Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act's 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The log₁₀ geometric means of E. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. The results indicate that upstream urban land was positively and significantly associated with the log₁₀ geometric mean concentrations of E. coli for all sampling cases and during storm events, respectively (p < 0.05). Prediction of E. coli concentrations by SSN models identified potential hot spots prone to water quality deterioration. The results emphasize that anthropogenic sources were the main threats to microbial water quality in the suburban Musconetcong River watershed. The SSN modeling approaches from this study can serve as a novel microbial water quality modeling framework for other watersheds to identify key land use stressors to guide future urban and suburban water quality restoration directions in the USA and beyond.

Soil erosion risk for farming futures: Novel model application and validation to an agricultural landscape in southern England

Increasingly, agricultural land managers are seeking new approaches for understanding the potential challenges posed by sediment connectivity across catchments from source to sink, and implications for delivery of ecosystem services determined by the condition of natural capital assets. Connectivity indices have been frequently applied in the calculation of risk in spatial and temporal assessment frameworks, and tools which facilitate rapid modelling and mapping of soil erosion risk using broad-scale environmental data are therefore of considerable interest. One such indicative tool is SCIMAP (Sensitive Catchment Integrated Mapping and Analysis Platform), which highlights where sediment runoff is likely to occur and be delivered to a watercourse by simulating the generation of saturation-excess overland flow. In this paper, we examine the utility of SCIMAP for exploring the changing nature of soil erosion risk as a function of land use change in the lower Rother catchment in West Sussex, southern England through the formulation of a suite of foresight scenarios informed by knowledge of historical land cover conditions and current management practice. The study area has previously been investigated at the field scale in terms of locating and quantifying sources of erosion and areas where in-stream sedimentation manifests. Output risk values from all simulations were quantified, mapped and compared to highlight areas of greatest/lowest risk. An area was identified immediately north of the main Rother channel that consistently exhibited greatest risk across each land cover scenario. We explore (i) the spatial and temporal variation in modelled risk and (ii) the utility value of SCIMAP for agricultural land-managers and policy-makers in generating robust risk estimates of soil erosion and in-stream sedimentation, and challenges with model verification in a foresight context.

Persistence of Salmonella Typhimurium and antibiotic resistance genes in different types of soil influenced by flooding and soil properties

Salmonella is a zoonotic foodborne bacterial pathogen that can seriously harm health. Persistence of Salmonella and antibiotic resistance genes (ARGs) in different types of soil under flooding and natural conditions are rare explored. This study investigated the dynamic changes of the Salmonella, ARGs and bacterial communities in three types of soils applied with pig manure in lab scale. Abundance of the Salmonella Typhimurium in soils reduced to the detection limit varied from 40 to 180 days, most of the Salmonella did not survive in soil for more than 90 days. Flooding and soil texture (content of sand) promote the decline rate of Salmonella. No Salmonella was found have acquired resistance gene from the soil or manure after 90 days. 64 ARGs and 11 MGEs were quantified, abundance of these genes and risky ARGs both gradually decline along with the extension of time. Most of the extrinsic ARGs cannot colonize in soil, cellular protection and antibiotic deactivation were their main resistance mechanism. Multidrug resistance and efflux pump were the dominant class and mechanism of soil intrinsic ARGs. Flooding can affect the ARGs profiles by reducing the types of extrinsic ARGs invaded into soil and inhibit the proliferation of intrinsic genes. Soil sand content, soil moisture and nutrition concentrations had significant direct effect on the abundance or profile of ARGs. Soil bacterial community structures also changed along with the extension of time and affected by flooding. Network analyses between ARGs and bacteria taxa revealed that Actinobacteria and Myxococcia were the main hosts of intrinsic ARGs, some taxa may play a role in inhibiting extrinsic ARGs colonization in the soils. These findings unveil that saturate soil with water may play a positive role in reducing potential risk of Salmonella and ARGs in the farmland environment.

Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

The dynamics of fecal indicator bacteria, such as fecal coliforms (FC) in streams, are influenced by the interactions of a myriad of factors. To predict complex spatiotemporal patterns of FC in streams and assess the relative importance of numerous controlling factors, the adoption of a hierarchical Bayesian network (HBN) was proposed in this study. By introducing latent variables correlated to the observed variables into a Bayesian network, the HBN can represent causal relationships among a large set of variables with a multilevel hierarchy. The study area encompasses 215 sites across the watersheds of the four major rivers in South Korea. The monitoring data collected during the 2012-2019 period included 32 input variables pertaining to meteorology, geography, soil characteristics, land cover, urbanization index, livestock density, and point sources. As model endpoints, the exceedance probability of the FC standard concentration as well as two pollution characteristics (i.e., pollution degree and type), derived from FC load duration curves were used. The probability of exceeding an FC threshold value (200 CFU/100 mL) showed spatiotemporal variations, whereas pollution degree and type showed spatial variations that represent long-term severity and relative dominance of nonpoint and point source fecal pollution, respectively. The conceptual model was validated using structural equation modeling to develop the HBN. The results demonstrate that the HBN effectively simplified the model structure, while showing strong model performance (AUC = 0.81, accuracy = 0.74). The results of the sensitivity analysis indicate that land cover is the most important factor in predicting the probability of exceedance and pollution degree, whereas the urbanization index explains most of the variability in pollution type. Furthermore, the results of the scenario analysis suggest that the HBN provides an interpretable framework in which the interaction of controlling factors has causal relationships at different levels that can be identified and visualized.

Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments

Microbial pollution in rivers poses known ecological and health risks, yet causal and mechanistic linkages to sources remain difficult to establish. Host-associated microbial source tracking (MST) markers help to assess the microbial risks by linking hosts to contamination but do not identify the source locations. Land-use regression (LUR) models have been used to screen the source locations using spatial predictors but could be improved by characterizing transport (i.e., hauling, decay overland, and downstream). We introduce the microbial Find, Inform, and Test (FIT) framework, which expands previous LUR approaches and develops novel spatial predictor models to characterize the transported contributions. We applied FIT to characterize the sources of BoBac, a ruminant Bacteroides MST marker, quantified in riverbed sediment samples from Kewaunee County, Wisconsin. A 1 standard deviation increase in contributions from land-applied manure hauled from animal feeding operations (AFOs) was associated with a 77% (p-value <0.05) increase in the relative abundance of ruminant Bacteroides (BoBac-copies-per-16S-rRNA-copies) in the sediment. This is the first work finding an association between the upstream land-applied manure and the offsite bovine-associated fecal markers. These findings have implications for the sediment as a reservoir for microbial pollution associated with AFOs (e.g., pathogens and antibiotic-resistant bacteria). This framework and application advance statistical analysis in MST and water quality modeling more broadly.

The role of biofilm in the development and dissemination of ubiquitous pathogens in drinking water distribution systems: an overview of surveillance, outbreaks, and prevention

A variety of pathogenic microorganisms can survive in the drinking water distribution systems (DWDS) by forming stable biofilms and, thus, continually disseminating their population through the system's dynamic water bodies. The ingestion of the pathogen-contaminated water could trigger a broad spectrum of illnesses and well-being-related obstacles. These waterborne diseases are a significant concern for babies, pregnant women, and significantly low-immune individuals. This review highlights the recent advances in understanding the microbiological aspects of drinking water quality, biofilm formation and its dynamics, health issues caused by the emerging microbes in biofilm, and approaches for biofilm investigation its prevention and suppression in DWDS.

Chronic urban hotspots and agricultural drainage drive microbial pollution of karst water resources in rural developing regions

Contamination of surface and groundwater systems with human and animal faecal matter leads to exposure of reliant populations to disease causing micro-organisms. This exposure route remains a major cause of infection and mortality in developing countries, particularly rural regions. To meet the UN's sustainable development goal 6: Ensure availability and sustainable management of water and sanitation for all, we need to identify the key controls on faecal contamination across relevant settings. We conducted a high-resolution spatial study of E. coli concentration in catchment drainage waters over 6 months in a mixed land-use catchment in the extensive karst region extending across impoverished southwest China. Using a mixed effects modelling framework, we tested how land-use, karst hydrology, antecedent meteorological conditions, agricultural cycles, hydrochemistry, and position in the catchment system affected E. coli concentrations. Land-use was the best predictor of faecal contamination levels. Sites in urban areas were chronically highly contaminated, but water draining from agricultural land was also consistently contaminated and there was a catchment wide pulse of higher E. coli concentrations, turbidity, and discharge during paddy field drainage. E. coli concentration increased with increasing antecedent rainfall across all land-use types and compartments of the karst hydrological system (underground and surface waters), but decreased with increasing pH. This is interpreted to be a result of processes affecting pH, such as water residence time, rather than the direct effect of pH on E. coli survival. Improved containment and treatment of human waste in areas of higher population density would likely reduce contamination hotspots, and further research is needed to identify the nature and distribution of sources in agricultural land.

Impacts of different sources of animal manures on dissemination of human pathogenic bacteria in agricultural soils

The human pathogenic bacteria (HPB) in animal feces may disseminate to agricultural soils with their land application as organic fertilizer. However, the knowledge about the impacts of different sources and rates of animal manures on the temporal changes of soil HPB remains limited, which hamper our ability to estimate the potential risks of their land application. Here, we constructed an HPB database including 565 bacterial strains. By blasting the 16 S rRNA gene sequences against the database we explored the occurrence and fate of HPB in soil microcosms treated with two rates of swine, poultry or cattle manures. A total of 30 HPB were detected in all of manure and soil samples. Poultry manure at the high level obviously improved the abundance of soil HPB. The application of swine manure could introduce concomitant HPB into the soils. Of which, Pseudomonas syringae pv. syringae B728a and Escherichia coli APEC O78 may deserve more attention because of their survival for a few days in manured soils and being possible hosts of diverse antibiotic resistance genes (ARGs) as revealed by co-occurrence pattern. Bayesian source tracking analysis showed that the HPB derived from swine manure had a higher contribution to soil pathogenic communities than those from poultry or cattle manures in early days of incubation. Mantel test together with variation partitioning analysis suggested that bacterial community and soil physicochemical properties were the dominant factors determining the profile of HPB and contributed 64.7% of the total variations. Overall, our results provided experimental evidence that application of animal manures could facilitate the potential dissemination of HPB in soil environment, which should arouse sufficient attention in agriculture practice and management to avoid the threat to human health.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 2: Application to a small agricultural catchment

The new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is applied to a small (0.42 km²) Scottish agricultural catchment to simulate the dynamics of E. coli arising from sheep and cattle farming, in order to provide a proof-of-concept. The hydrological environment for MAFIO was simulated by the "best" ensemble run of the tracer-aided ecohydrological model EcH₂O-iso, obtained through multi-criteria calibration to stream discharge (MAE: 1.37 L s^-1) and spatially-distributed stable isotope data (MAE: 1.14-3.02‰) for the period April-December 2017. MAFIO was then applied for the period June-August for which twice-weekly E. coli loads were quantified at up to three sites along the stream. Performance in simulating these data suggested the model has skill in capturing the transfer of faecal indicator organisms (FIOs) from livestock to streams via the processes of direct deposition, transport in overland flow and seepage from areas of degraded soil. Furthermore, its agent-based structure allowed source areas, transfer mechanisms and host animals contributing FIOs to the stream to be quantified. Such information is likely to have substantial value in the context of designing and spatially-targeting mitigation measures against impaired microbial water quality. This study also revealed, however, that avenues exist for improving process conceptualisation in MAFIO (e.g. to include FIO contributions from wildlife) and highlighted the need to quantitatively assess how uncertainty in the spatial extent of surface flow paths in the simulated hydrological environment may affect FIO simulations. Despite the consequent status of MAFIO as a research-level model, its encouraging performance in this proof-of-concept study suggests the model has significant potential for eventual incorporation into decision support frameworks.

Microbial Indicators of Fecal Pollution: Recent Progress and Challenges in Assessing Water Quality

PURPOSE OF REVIEW

Fecal contamination of water is a major public health concern. This review summarizes recent developments and advancements in water quality indicators of fecal contamination.

RECENT FINDINGS

This review highlights a number of trends. First, fecal indicators continue to be a valuable tool to assess water quality and have expanded to include indicators able to detect sources of fecal contamination in water. Second, molecular methods, particularly PCR-based methods, have advanced considerably in their selected targets and rigor, but have added complexity that may prohibit adoption for routine monitoring activities at this time. Third, risk modeling is beginning to better connect indicators and human health risks, with the accuracy of assessments currently tied to the timing and conditions where risk is measured. Research has advanced although challenges remain for the effective use of both traditional and alternative fecal indicators for risk characterization, source attribution and apportionment, and impact evaluation.

Rainfall-driven E. coli transfer to the stream-conduit network observed through increasing spatial scales in mixed land-use paddy farming karst terrain

Karst aquifers have distinctive hydrology and supply 25% of the world's population with drinking water, making them a critical geological setting for understanding and managing microbial water pollution. Rainfall causes elevated concentrations and loading of faecal microorganisms, e.g. E. coli, in catchment surface and groundwater systems, increasing the risk of human exposure to faecally-contaminated water. However, effective management of microbial water quality in complex karst catchments is constrained by limited understanding of E. coli - discharge responses to rainfall. We analysed how rainfall events of varying magnitude (2.4-100 mm) control E. coli-discharge dynamics at increasing spatial scales in a mixed land-use karst catchment in southwest China. During the wet season, hourly water sampling was undertaken throughout five storm events to characterise in high detail E. coli emergence with resulting flow across multiple sites of varying catchment area, stream order, and land-use. E. coli concentration was found to increase by 1-3 orders of magnitude following rainfall events. Maximum E. coli concentration and speed of E. coli recession were influenced by rainfall (amount, intensity), timing of agricultural activities, and position in the hydrological system. For high intensity events ∼90% of the cumulative E. coli export occurred within 48 h. E. coli concentration increased with increasing discharge at all sites. E. coli concentration at low discharge was higher in the headwaters than at the catchment outlet, while the rate of increase in E. coli concentration with increasing discharge appears to follow the opposite trend, being higher at the catchment outlet than the headwaters. This was attributed to the decreasing flow path gradient and increasing degree of development of the fissure network, but further event monitoring at varying catchment scales is required to confirm this relationship. The results provide novel insight into how rainfall characteristics combine with land-use and catchment hydrology to control E. coli export in karst landscapes.

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock

Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen &Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1km² grid cell (Ayr: r=0.57; p<0.001, Lunan: r=0.32; p<0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P<0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments.