Characterizing Differences in Sources of and Contributions to Fecal Contamination of Sediment and Surface Water with the Microbial FIT Framework

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.

Forecasting acute rainfall driven E. coli impacts in inland rivers based on sewer monitoring and field runoff

Surface water quality is frequently impacted by acute rainfall driven pollutant sources such as sewer overflows. Understanding the risk of exposure from faecal pollution from short term impacts is challenging due to a paucity of high-resolution data from river systems. This paper proposes practical modelling approach for forecasting arrival time and durations of elevated E. coli levels based on hydrological routing of catchment source loadings, characterized by distributed and remote sensing techniques (including sewer overflow monitoring). The model is calibrated and validated using new high resolution E. coli datasets from a UK catchment featuring both diffuse field runoff and storm overflow impacts. Hourly/Bihourly sampling of E. coli was undertaken in the river following different rainfall events across a range of seasonal conditions. The model provides a good estimate of arrival times and durations of elevated E. coli periods following rainfall events. Model simulations suggest that key sources in the catchment are event specific, with sewer overflow spills being more significant following short, intense rainfall events.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework

Introduction

Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined.

Methods

To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region.

Results

A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34-80% and 33-77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km.

Discussion

Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.

Using watershed characteristics to enhance fecal source identification

Fecal pollution is one of the most prevalent forms of pollution affecting waterbodies worldwide, threatening public health and negatively impacting aquatic environments. Microbial source tracking (MST) applies polymerase chain reaction (PCR) technology to help identify the source of fecal pollution. In this study, we combine spatial data for two watersheds with general and host-associated MST markers to target human (HF183/BacR287), bovine (CowM2), and general ruminant (Rum2Bac) sources. Concentrations of MST markers in samples were determined with droplet digital PCR (ddPCR). The three MST markers were detected at all sites (n = 25), but bovine and general ruminant markers were significantly associated with watershed characteristics. MST results, combined with watershed characteristics, suggest that streams draining areas with low-infiltration soil groups and high agricultural land use are at an increased risk for fecal contamination. Microbial source tracking has been applied in numerous studies to aid in identifying the sources of fecal contamination, but these studies usually lack information on the involvement of watershed characteristics. Our study combined watershed characteristics with MST results to provide more comprehensive insight into the factors that influence fecal contamination in order to implement the most effective best management practices.

Exposure to industrial hog and poultry operations and urinary tract infections in North Carolina, USA

An increasing share of urinary tract infections (UTIs) are caused by extraintestinal pathogenic Escherichia coli (ExPEC) lineages that have also been identified in poultry and hogs with high genetic similarity to human clinical isolates. We investigated industrial food animal production as a source of uropathogen transmission by examining relationships of hog and poultry density with emergency department (ED) visits for UTIs in North Carolina (NC). ED visits for UTI in 2016-2019 were identified by ICD-10 code from NC's ZIP code-level syndromic surveillance system and livestock counts were obtained from permit data and aerial imagery. We calculated separate hog and poultry spatial densities (animals/km2) by Census block with a 5 km buffer on the block perimeter and weighted by block population to estimate mean ZIP code densities. Associations between livestock density and UTI incidence were estimated using a reparameterized Besag-York-Mollié (BYM2) model with ZIP code population offsets to account for spatial autocorrelation. We excluded metropolitan and offshore ZIP codes and assessed effect measure modification by calendar year, ZIP code rurality, and patient sex, age, race/ethnicity, and health insurance status. In single-animal models, hog exposure was associated with increased UTI incidence (rate ratio [RR]: 1.21, 95 % CI: 1.07-1.37 in the highest hog-density tertile), but poultry exposure was associated with reduced UTI rates (RR: 0.86, 95 % CI: 0.81-0.91). However, the reference group for single-animal poultry models included ZIP codes with only hogs, which had some of the highest UTI rates; when compared with ZIP codes without any hogs or poultry, there was no association between poultry exposure and UTI incidence. Hog exposure was associated with increased UTI incidence in areas that also had medium to high poultry density, but not in areas with low poultry density, suggesting that intense hog production may contribute to increased UTI incidence in neighboring communities.