Poor sanitation in rural infrastructure is often associated with high levels of fecal contamination in adjacent surface waters, which presents a community health risk. Although microbial source tracking techniques have been widely applied to identify primary remediation needs in urban and/or recreational waters, use of human-specific markers has been more limited in rural watersheds. This study quantified the human source tracking marker Bacteroides-HF183, along with more general fecal indicators (i.e. culturable Escherichia coli and a molecular Enterococcus marker), in two Appalachian streams above and below known discharges of untreated household waste. Although E. coli and Enterococcus were consistently recovered in samples collected from both streams, Bacteroides-HF183 was only detected sporadically in one stream. Multiple linear regression analysis demonstrated a positive correlation between the concentration of E. coli and the proximity and number of known waste discharge points upstream; this correlation was not significant with respect to Bacteroides-HF183, likely due to the low number of quantifiable samples. These findings suggest that, while the application of more advanced source targeting strategies can be useful in confirming the influence of substandard sanitation on surface waters to justify infrastructure improvements, they may be of limited use without concurrent traditional monitoring targets and on-the-ground sanitation surveys.

INTRODUCTION

Despite major advancements in wastewater treatment in the past century, inadequate sanitation remains an issue for some communities in the United States. Infrastructure for sanitation is particularly lacking in remote communities where there are fewer users to cover infrastructure costs and technical assistance is not readily available locally (Tarricone 1989; Gasteyer & Vaswani 2004). This can result in inadequate wastewater management strategies that direct untreated household waste to local surface waters with minimal pre-treatment (e.g. soakaways, straight pipes), posing potential health risks to adjacent and downstream communities.

Improper treatment of household wastewater has long been recognized as a public health hazard. In the most recently identifiable national survey of medical care providers in rural communities, surface water pollution was identified as a primary environmental health concern (Robson & Schneider 2001). Waste entering common waterways used for recreation or drinking water can be a point of human exposure for infectious disease, particularly in low population areas (Denno et al. 2009; Collier et al. 2012). Relative to less developed countries, the incidence of waterborne diseases such as hepatitis A, salmonellosis, and typhoid is very low in the United States, but real risks do exist in areas without proper water and sanitation (Gasteyer & Vaswani 2004). Eliminating these sources of pathogen contamination could result in both significant public health and economic benefits, as waterborne illnesses have multiple adverse economic effects including the cost of hospitalizations, losses in productivity at places of employment, and absences from school (Collier et al. 2012).

A particularly unique set of conditions exists in rural communities in central Appalachia. In this region, underlying karst soils are often inadequate to support traditional on-site wastewater treatment strategies such as septic systems, i.e. the soils are too thin to provide sufficient biological treatment prior to discharge to the environment. Furthermore, in narrow Appalachian valleys, residences are often located too close to streams to permit the construction of proper drain fields (Cook et al. 2015). Other on-site solutions, such as package treatment plants or sand filters have seen limited use, but are often precluded by distinctly local challenges (e.g. the necessity of shared responsibility for installation and maintenance in already resource-stressed communities; Tarricone 1989). When traditional municipal or onsite septic systems are not readily available, untreated household waste may simply be ‘straight piped’ into nearby surface waters (Banks et al. 2005). The number of straight pipes in the entire region is unknown, but the practice is not uncommon, e.g. in Letcher County, KY, one survey estimated 3,000 straight pipes serving about 40% of 30,000 total residents (Glasmeier & Farrigan 2003). Though the discharge of untreated household wastewater is intuitively expected to result in significant deterioration of receiving water quality, little monitoring data describing the magnitude of impact is available.

Standard monitoring practices to identify water quality impairments generally only target fecal indicator bacteria (FIB) such as total coliforms or Escherichia coli. While generally non-pathogenic themselves, these are considered sentinels of potential pathogen presence and associated human health risks. However, because these organisms are common to the intestinal tract of all warm-blooded animals, their presence provides limited information regarding the relative contributions of specific sources of fecal contamination (e.g. human vs. animal). Though the transfer of zoonotic pathogens is important, fecal contamination by human sources is generally considered of greatest health concern since many waterborne pathogens (viruses in particular) exhibit host specificity (Harwood et al. 2014). The inclusion of library-independent, established source-specific markers in monitoring efforts can greatly aid in watershed assessment: understanding the origin of fecal contamination can improve local understanding of potential health risks and inform decisions about appropriate remediation actions to meet water quality goals (Scott et al. 2002). At present, the genetic marker HF183 from the anaerobic Bacteroides spp. is well established as strongly indicative of the presence of human fecal contamination in waters with multiple potential contamination (Seurinck et al. 2005; Harwood et al. 2009, 2014; Boehm et al. 2013; Sidhu et al. 2013). Because the majority of prior studies have tracked Bacteroides-HF183 concentrations downstream from relatively large centralized wastewater treatment facilities or highly urbanized areas, the relationship between FIB, source tracking markers, and health risks in less populated regions is poorly understood (Harwood et al. 2009). However, application of these more advanced monitoring techniques in rural regions with known sanitation issues could be useful in tracking the downstream reach of contamination from seemingly isolated communities and justifying and/or evaluating infrastructure improvements. In addition, these relatively simple and sparsely populated systems represent a unique opportunity to evaluate the robustness of common microbial source tracking (MST) markers compared to settings in which they are more commonly used.

This study aimed to directly link microbial measures of fecal contamination with the prevalence of straight pipes in two central Appalachian streams. Specific objectives included: (1) detection of the human source tracking marker Bacteroides-HF183 in receiving waters polluted by untreated household waste from rural communities; (2) assessment of the co-occurrence of Bacteroides-HF183 with E. coli and Enterococcus, which are currently recommended by regulatory agencies as sentinels of fecal contamination and associated public health risks based on culturable and molecular targets, respectively (USEPA 2012; Haugland et al. 2013); (3) exploration of correlations between microbial indicators and physiochemical water quality parameters; and (4) development of a spatial statistical strategy to link straight pipe locations to longitudinal downstream measures of E. coli in these streams.

METHODS

Site description

The study area consists of two 12-digit hydrological unit code (HUC) watersheds in southwestern Virginia (Figure 1): one drains to Callahan Creek and the other to Roaring Fork. Both of these streams are tributaries to the Powell River basin in Virginia. Per the National Land Cover Database, the land cover mainly consists of hardwood deciduous forest (66%). There is very little agricultural activity (0.5%), but significant surface mining (17%). The remainder of the land cover is composed of grassland (14.5%) and development (2%). Development in both watersheds is mainly concentrated adjacent to the streams in the flatter, more habitable areas known as mountain hollows. Samples were collected directly above and below residential areas or any known discharges of fecal contamination, as well as at the top of the watershed and watershed outlet, for a total of 14 in-stream sampling sites in each watershed.
Figure 1

Sampling sites and known discharges for 12-digit HUC watersheds Callahan Creek (a) and Roaring Fork (b). *Indicates the site was tested for both molecular markers (Bacteroides-HF183 and Enterococcus) as well as E. coli.

Figure 1

Sampling sites and known discharges for 12-digit HUC watersheds Callahan Creek (a) and Roaring Fork (b). *Indicates the site was tested for both molecular markers (Bacteroides-HF183 and Enterococcus) as well as E. coli.

Sample collection

Water samples were collected monthly from August 2012 until August 2014 and from January 2016 to April 2016. For all months and at all sites, samples were collected for E. coli analysis (n = 28 for each site), and in-stream temperature, dissolved oxygen, pH and specific conductivity were measured on-site using a YSI Quattro Pro Plus (YSI, Yellow Springs, OH, USA). Between April 2014 and August 2014 and January 2016 to April 2016, an additional sample aliquot was collected for subsequent molecular analyses (n = 9) from five sites in each watershed. All samples for microbiological analysis were collected at the thalweg in 250 mL pre-sterilized bottles, and transported on ice back to the Biological Systems Engineering Seitz Hall laboratory at Virginia Tech for prompt analysis (i.e. within 6 hours for culturable microorganisms; filtration for molecular preservation within 48 hours).

Microbial analyses

Analysis for E. coli concentrations occurred immediately upon return to the laboratory via the Colilert defined substrate method using the Quanti-Tray/2000 (IDEXX, Westbrook, ME, USA). Following 24 hours incubation at 37 °C, a most probable number (MPN) of bacteria could be determined based on the number of positive wells (i.e. fluorescent under UV light).

For samples collected for molecular analyses, two additional 100 mL aliquots from each sample were vacuum filtered through a Millipore 0.4 μm filter (Merck KGaA, Darmstadt, Germany). Filter effluent was discarded and filters were stored in cryotubes at −80 °C prior to DNA extraction. Within six months, DNA from the filters was extracted using a PowerWater DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA). Copy numbers of Enterococcus were quantified via quantitative polymerase chain reaction (qPCR) as described previously by Haugland et al. (2005). Copy numbers of the human-specific Bacteroides-HF183 MST marker were quantified by the procedures outlined in Griffith et al. (2013) and Seurinck et al. (2005) (Table 1). Samples were run in triplicate, with each well containing 20 μL of Master Mix and 5 μL of sample. A no-template control was tested along with the samples to indicate any sources of contamination. A base plasmid containing the target marker at known amounts was used to create a standard calibration curve. Results were considered valid if the run-specific standard curve had an R2 value above 0.99 and efficiency between 0.90 and 1.10. The Bacteroides-HF183 master mix also included a plasmid containing an internal amplification control (IAC) sequence to quantify inhibition (Griffith et al. 2013). The limit of detection (LOD) for Bacteroides-HF183 was determined by scaling the lowest detectable amount of Bacteroides-HF183 in tested samples to the total amount of eluted sample from DNA extraction. The same process was repeated with the lowest value on the standard curve (10 copies/mL) to determine the limit of quantification (LOQ).

Table 1

Master mix composition for Bacteroides-HF183 and Enterococcus qPCR procedures

Target gene Primer sequences and sources Reaction mixture Volume (μL) Thermal profile 
Bact. HF183  Primer probe mix 50 °C – 2 min, 95 °C – 10 min, 40 Cycles 95 °C – 15 sec 60 °C – 1 min 
5′- ATCATGAGTTCACATGTCCG -3′  HF-183 10 
5′- CTTCCTCTCAGAACCCCTATCC -3′  BacR287 10 
[6FAM] - 5′- CTAATGGAACGCATCCC –MGB  BacP234MGB 
[VIC]-5′- AACACGCCGTTGCTACA –MGB  Bac234IAC 
 PCR-grade water 
(Seurinck et al. 2005; Griffith et al. 2013SsoAdvanced Universal Probes Mix 12.5 
 Bovine serum albumin (BSA) 2.5 
 Plasmid with IAC 
Ent.  Primer probe mix 50 °C – 2 min, 95 °C – 10 min, 40 Cycles 95 °C – 15 sec 60 °C – 1 min 
5′-GAGAAATTCCAAACGAACTTG-3′  ECST748F 10 
5′-CAGTGCTCTACCTCCATCATT-3′  ENC854R 10 
5′-TGGTTCTCTCCGAAATAGCTTTAGGGCTA-3′  GLP813TQ 
 PCR grade water 
(Haugland et al. 2005BSA 1.5 
 iTaq Universal Probe Mix 12.5 
Target gene Primer sequences and sources Reaction mixture Volume (μL) Thermal profile 
Bact. HF183  Primer probe mix 50 °C – 2 min, 95 °C – 10 min, 40 Cycles 95 °C – 15 sec 60 °C – 1 min 
5′- ATCATGAGTTCACATGTCCG -3′  HF-183 10 
5′- CTTCCTCTCAGAACCCCTATCC -3′  BacR287 10 
[6FAM] - 5′- CTAATGGAACGCATCCC –MGB  BacP234MGB 
[VIC]-5′- AACACGCCGTTGCTACA –MGB  Bac234IAC 
 PCR-grade water 
(Seurinck et al. 2005; Griffith et al. 2013SsoAdvanced Universal Probes Mix 12.5 
 Bovine serum albumin (BSA) 2.5 
 Plasmid with IAC 
Ent.  Primer probe mix 50 °C – 2 min, 95 °C – 10 min, 40 Cycles 95 °C – 15 sec 60 °C – 1 min 
5′-GAGAAATTCCAAACGAACTTG-3′  ECST748F 10 
5′-CAGTGCTCTACCTCCATCATT-3′  ENC854R 10 
5′-TGGTTCTCTCCGAAATAGCTTTAGGGCTA-3′  GLP813TQ 
 PCR grade water 
(Haugland et al. 2005BSA 1.5 
 iTaq Universal Probe Mix 12.5 

Statistical methods

All statistical analyses were conducted in JMP 12 (SAS, Cary, NC, USA). Normality/non-normality of each dataset was determined via observation of the normal quantile plot prior to analysis. Correlations between molecular targets, E. coli, and general measures of water quality were assessed via Spearman's rank correlation.

A statistical mixed-effects model was developed to determine if concentrations of microbial markers were significantly related to the number of waste discharges upstream of each site, the distance from each site to known waste discharges upstream, and the season (winter/‘high flow’ vs not-winter/‘low flow’). Previous studies have performed multidimensional regression to create predictive models linking concentrations of bacteria to explanatory factors (e.g. water quality, land use (Hampson et al. 2010; Herrig et al. 2015)). The model used here is not intended to be predictive, but to identify and define variables that are statistically explanatory: 
formula
1

Fixed variables included the sum of inverse distances to all upstream discharges of untreated household waste (D), as well as a binary variable indicating whether the season was winter (S; where December, January and February were considered winter months). Although the location of known waste discharges included both septic tanks and straight pipes, the effects from these sources were not differentiated, as local health department sanitary surveys suggest most local septics do not meet codes and/or are failing. Random variables were normally distributed and centered at 0, and accounted for dependence of measurements taken within sites () as well as possible sources of random error (). Coefficients ( and ) were determined by the model to describe the behavior of the S and D variables and the general direction of the correlations.

RESULTS AND DISCUSSION

Detection and longitudinal trends of microbial indicators

Bacteroides-HF183 was detected in samples collected in Callahan Creek (Figure 2(a)) but was not found in any samples collected in Roaring Fork; analysis of the IAC did not indicate significant inhibition in any of the samples. The genetic marker, Bacteroides-HF183, is known to be present at varying concentrations in different individuals (Field 2002). When tracking sewage contamination from large concentrated communities (e.g. combined sewer overflows in cities) the marker is present in the population at high enough concentrations to be reliable. However, because HF183 is not ubiquitous in the human intestinal tract, its use in tracking waste from more isolated rural communities may be more limited. Though both study watersheds are quite rural, Callahan Creek does have roughly double the population of Roaring Fork with a similar area (180 vs 80 households, counted on Google maps satellite imagery). The population of Roaring Fork may simply be too low to produce measurable levels of HF183 downstream.
Figure 2

Longitudinal profile of Bacteroides-HF183 (a), Enterococcus (b) and E. coli (c) in Callahan Creek. Distance is measured along the stream starting from the most upstream sampling point. The data points are extrapolated below the LOQ. The number of samples below the LOD at each site is indicated by an n value (n = 9 samples in total for each site). This does not include samples taken directly from household wastewater discharges.

Figure 2

Longitudinal profile of Bacteroides-HF183 (a), Enterococcus (b) and E. coli (c) in Callahan Creek. Distance is measured along the stream starting from the most upstream sampling point. The data points are extrapolated below the LOQ. The number of samples below the LOD at each site is indicated by an n value (n = 9 samples in total for each site). This does not include samples taken directly from household wastewater discharges.

As illustrated in Figure 2(a), Bacteroides-HF183 concentrations were negligible at the most upstream point in Callahan Creek, which was expected given that this sampling site is above the known discharge points of untreated household waste. The next sampling site is directly below a residential community, and shows a spike in the concentrations of Bacteroides-HF183. Samples taken directly from the household waste effluent discharge to Callahan Creek ranged from detectable but not quantifiable (DNQ) to 59,400 copies per mL. Though this value may appear high, it is notably lower than typical values of Bacteroides-HF183 in raw sewage which have been observed between copies/mL (e.g. Seurinck et al. 2005). In Callahan Creek itself, Bacteroides-HF183 levels remained between 10 and 103 copies/mL (43% positive samples and 13% quantifiable samples) until the furthest downstream sampling site, where it was again below the LOD. Observed levels are similar to those reported by Tambalo et al. (2012), in a rural environment with known discharges of human waste in Saskatchewan, Canada. In this study, Bacteroides was detected via qPCR of the genetic marker BacH, at concentrations at or below 104 copies/mL in different sites at positive detection rates between 25 and 47% of collected samples. In contrast, a recent urban study by Sidhu et al. (2013), reported Bacteroides-HF183 in over 90% of the samples adversely impacted by untreated or partially treated human waste. Denser population centers with sewage intrusion appear to result in more successful detection of Bacteroides-HF183 and higher concentrations in receiving waters.

The Enterococcus molecular marker was detected from samples in both Callahan Creek and Roaring Fork (Figures 2(b) and 3(a) respectively). Similar to Bacteroides-HF183 in Callahan Creek, Enterococcus showed a spike in detection in samples directly downstream from residential communities. However, unlike Bacteroides-HF183, Enterococcus did not return to background concentrations at the watershed outlet. This trend is similar to that of E. coli, which also was elevated directly downstream from residential areas, and remained higher than its background level at the watershed outlet in both Callahan Creek and Roaring Fork (Figures 2(c) and 3(b) respectively).
Figure 3

Longitudinal profile of Enterococcus (a) and E. coli (b) in Roaring Fork. Distance is measured along the stream starting from the most upstream sampling point. The data points are extrapolated below the LOQ. The number of samples below the LOD at each site is indicated by an n value. This does not include samples taken directly from household wastewater discharges.

Figure 3

Longitudinal profile of Enterococcus (a) and E. coli (b) in Roaring Fork. Distance is measured along the stream starting from the most upstream sampling point. The data points are extrapolated below the LOQ. The number of samples below the LOD at each site is indicated by an n value. This does not include samples taken directly from household wastewater discharges.

Both Enterococcus and E. coli are universally present in the human digestive system, and may also originate from wildlife or domestic animals, however, additional non-human significant discharges of E. coli are considered to be negligible in this area. Agricultural land use is low (0.5%), and samples collected in wholly forested areas upstream from the straight-piped communities generally showed barely detectable E. coli (geometric mean = 4 MPN/100 mL), suggesting low levels of background/wildlife. Longer longitudinal persistence in Enterococcus and E. coli levels therefore may be the result, at least partially, of longitudinal decay. Unlike Bacteroides, which is an obligatory anaerobe, E. coli and Enterococcus are facultative anaerobic bacteria, and therefore can survive in the aerobic surface water environment. Highly oxygenated mountain streams may cause particular stress to Bacteroides.

Temporal trends of microbial markers

Seasonal trends are not immediately apparent in the recovery of Bacteroides-HF183 and Enterococcus from Callahan Creek, though values varied considerably by month (Table 2). E. coli was generally lower during winter and early spring months (i.e. January–April) than late spring and summer months (i.e. May–August). There were two notably high values in June 2014 in consecutive sampling sites (i.e. CC09 and CC06). This is consistent with expectations since flow rate should generally increase during the colder/wetter month versus the warmer/drier months while bacterial loads remain constant, thereby resulting in negative correlations with the measured concentration of microbial indicators. However, positive samples were collected even during winter months, when the highest streamflow values would be expected to dilute discharges, and only weak correlations were found between E. coli and flow rate in Callahan Creek (Table 3). Therefore, while seasonal water quality parameters may provide some anecdotal explanation of variability in Bacteroides-HF183, Enterococcus and E. coli, additional factors such as sample collection timing are also likely important (i.e. collection directly following straight pipe discharge).

Table 2

Monthly Bacteroides-HF183, Enterococcus and E. coli in Callahan Creek

Bacteroides-HF183 (copies/mL)
 
Enterococcus (copies/mL)
 
E. coli (MPN/100 mL)
 
Date CC10 CC09 CC06 CC04 CC01 CC10 CC09 CC06 CC04 CC01 CC10 CC09 CC06 CC04 CC01 
4/14 − − − − − − − − 303 99 99 99 
5/14 − 200 − − − 62 2,461 1,099 631 2,846 
6/14 − 5,770 913 − − − − − 303 305 99 99 
7/14 − − − 1,230 − − − 12 3,068 201 1,463 98 
8/14 − − − − − 259 408 737 730 2,333 
1/16 − − − − − 1,380 206 236 96 115 
2/16 − 454 − − − 95 647 2,162 27 
3/16 − − − − − − − − 94 645 912 96 
4/16 − 200 − − − − − − 55 352 596 846 
Bacteroides-HF183 (copies/mL)
 
Enterococcus (copies/mL)
 
E. coli (MPN/100 mL)
 
Date CC10 CC09 CC06 CC04 CC01 CC10 CC09 CC06 CC04 CC01 CC10 CC09 CC06 CC04 CC01 
4/14 − − − − − − − − 303 99 99 99 
5/14 − 200 − − − 62 2,461 1,099 631 2,846 
6/14 − 5,770 913 − − − − − 303 305 99 99 
7/14 − − − 1,230 − − − 12 3,068 201 1,463 98 
8/14 − − − − − 259 408 737 730 2,333 
1/16 − − − − − 1,380 206 236 96 115 
2/16 − 454 − − − 95 647 2,162 27 
3/16 − − − − − − − − 94 645 912 96 
4/16 − 200 − − − − − − 55 352 596 846 

For Bacteroides-HF183 and Enterococcus, negative samples (i.e. less than 20 copies/mL) are indicated by ‘ − ’ and samples with 20–200 copies/mL (i.e. detected but not quantifiable) are indicated by ‘ + ’. Quantifiable samples show the number of copies.

Table 3

Results of Spearman's rho correlation analysis between microbial indicators and water quality parameters or flow

  Temp Cond DO pH Depth Flow ratea 
Callahan Creek 
 Bact. HF183 0.02 0.37b −0.16 0.06 0.01 0.11 
 E. coli 0.24b 0.20 −0.25b 0.21b −0.23b −0.19b 
 Enterococcus 0.14 0.01 −0.25 –0.01 0.24 −0.16 
Roaring Fork 
 E. coli 0.26b 0.03 −0.22b –0.13 0.13 −0.03 
 Enterococcus 0.302 –0.44b −0.31c –0.05 0.23 0.10 
  Temp Cond DO pH Depth Flow ratea 
Callahan Creek 
 Bact. HF183 0.02 0.37b −0.16 0.06 0.01 0.11 
 E. coli 0.24b 0.20 −0.25b 0.21b −0.23b −0.19b 
 Enterococcus 0.14 0.01 −0.25 –0.01 0.24 −0.16 
Roaring Fork 
 E. coli 0.26b 0.03 −0.22b –0.13 0.13 −0.03 
 Enterococcus 0.302 –0.44b −0.31c –0.05 0.23 0.10 

Bacteroides-HF183 is not shown for Roaring Fork since this marker was undetectable in all samples.

aFlow rate data from USGS gauge station 03529500 in the Powell River.

bSignificant correlation: p value <0.05.

cp value = 0.06.

Correlation of microbial indicators with water quality and flow rate

To investigate further, a correlational analysis was conducted between microbial indicators and several general water quality parameters and flow rate (Spearman's rho values, Table 3). Bacteroides-HF183 and conductivity were significantly and positively correlated in Callahan Creek (i.e. Spearman's rho = 0.37). This is consistent with expectations since both parameters can originate from the same source: untreated household waste. However, in Roaring Fork, conductivity and Enterococcus were inversely correlated (i.e. Spearman's rho = −0.44). This seems contradictory to expectations and perhaps suggested additional unattributed sources of conductivity (i.e. other than the untreated household waste discharges) in this stream (e.g. additional residences, adjacent railroad tracks).

A weak but significant correlation was observed between E. coli and pH in this stream (i.e. Spearman's rho = 0.21). In Callahan Creek, a slight spike in pH values was observed directly below the untreated household waste discharges, and then not far downstream a return to similar values as in the headwaters. The elevated pH near the waste discharges may be related to household chemical use or high pH in the water source for these residences (relative to the native stream pH). No significant correlation was found between pH and microbial indicators in Roaring Fork.

In both streams E. coli and temperature were significantly positively correlated (i.e. Spearman's rho of 0.24 in Callahan Creek and 0.26 in Roaring Fork) which may reflect higher detection during low flow warmer months. In keeping with this, flow rate was identified as an important seasonal variable in Callahan Creek. A negative correlation between the flow rate and E. coli (i.e. Spearman's rho = −0.19), as well as depth and E. coli (i.e. Spearman's rho = −0.23), was observed for that stream. In both streams, a significant and negative correlation was also observed between E. coli and dissolved oxygen (i.e. Spearman's rho of −0.25 in Callahan Creek and −0.22 in Roaring Fork), which could be related to biological and/or chemical oxygen demand contributed by untreated household waste.

The fact that multiple significant correlations were found between E. coli and other parameters, whereas the genetic markers were only correlated to conductivity, is likely due to the relatively higher number of quantifiable data points available for E. coli. The higher number of stronger correlations observed between microbial indicators and other parameters in Callahan Creek as compared to Roaring Fork may simply be related to the relatively larger number of untreated household waste discharges, and thus presumably higher waste volume.

Correlation analysis of microbial indicators

No positive correlations between E. coli and the molecular markers were identified when using only those data points above the LOQ; this is not surprising, as few samples were quantifiable via qPCR for either Bacteroides-HF183 or Enterococcus. When DNQ points (e.g. 20–200 copies/mL) were extrapolated to values below the LOQ, significant (p < 0.05) positive correlations were identified in Callahan Creek between Bacteroides-HF183 and E. coli (ρ = 0.4) and Enterococcus and E. coli (ρ = 0.37). The relationship between Enterococcus and E. coli (ρ = 0.43) was also significant in Roaring Fork (Figure 4).
Figure 4

Bacteroides-HF183 vs. E. coli (a), Enterococcus vs. E. coli (b) and Enterococcus vs. Bacteroides-HF183 (c) in Callahan Creek. Data is extrapolated below the LOQ.

Figure 4

Bacteroides-HF183 vs. E. coli (a), Enterococcus vs. E. coli (b) and Enterococcus vs. Bacteroides-HF183 (c) in Callahan Creek. Data is extrapolated below the LOQ.

All of the microbial indicators targeted can originate from human fecal contamination, and therefore a positive correlation between them would be expected; however, previous examinations of potential correlations between culturable indicator bacteria and genetic markers used in source tracking have been inconsistent, which may be a result of the inherent differences in analytical targets (Harwood et al. 2014). Culture-based methods only indicate the presence of viable organisms while non-culture based methods may detect non-viable strands of DNA; the two types of targets may have different rates of decay and/or transport mechanisms. In a laboratory study in Gulf of Mexico, Harwood et al. (2009) did not find any correlation between Enterococcus and human associated Bacteroidales, nor did Sidhu et al. (2013) find a significant correlation between E. coli and Enterococcus in a field study in commercial and residential catchments. Kapoor et al. (2013) found a moderate correlation (r2 = 0.17) between human associated Bacteroidales and E. coli genetic markers (i.e. uidA gene) downstream from combined sewage overflows in an urban environment. The only attempt at a similar analysis in a rural environment (Tambalo et al. 2012) found no correlation between the Bacteroides marker BacH and E. coli.

Association of microbial levels with untreated household waste via regression model

The sum of the inverse distances to all upstream sources of untreated household waste and the concentration of E. coli was significantly and positively correlated (p < 0.05) for both Callahan Creek and Roaring Fork (Table 4). There was also a significant, positive relationship between the concentrations of E. coli and seasonality for Callahan Creek, though this relationship was not significant for Roaring Fork. Water temperature in the streams and flow in the Powell River (the receiving river for both Callahan Creek and Roaring Fork) were tested in both models but did not show a significant relationship with the concentrations of E. coli and therefore were not included. Similar analyses with Bacteroides-HF183 or Enterococcus as the dependent variables were not significant due to the high level of non-detectable results that skewed the dataset.

Table 4

Callahan Creek and Roaring Fork regression model coefficients

Variable Callahan Creek coefficient Callahan Creek P value Roaring Fork coefficient Roaring Fork P value 
Intercept 2.69 <0.0001 0.89 <0.0001 
Not wintera 0.84 <0.0001 N/A >0.05 
Waste discharge valueb 0.08 <0.0001 0.07 <0.0001 
Variable Callahan Creek coefficient Callahan Creek P value Roaring Fork coefficient Roaring Fork P value 
Intercept 2.69 <0.0001 0.89 <0.0001 
Not wintera 0.84 <0.0001 N/A >0.05 
Waste discharge valueb 0.08 <0.0001 0.07 <0.0001 

aCategorical variable indicates whether the season is not winter, when true the season is not winter, when false the season is winter.

bVariable represents the sum of the inverse distances to all known upstream sources of untreated household waste.

The significant waste discharge number variable indicated that with more discharges upstream from a sampling site, the concentration of E. coli is likely to be higher, as would be expected. It also indicated that E. coli concentrations decrease as distance from discharges increases, which may be attributable to die-off and/or dilution, given the previously mentioned lack of other appreciable inputs of fecal contamination (e.g. wildlife). The model also confirmed that concentrations of E. coli are significantly lower during the winter in Callahan Creek, which is consistent with the expectation that these point sources should be diluted by generally higher streamflow values in the winter months and/or lower temperatures may be affecting levels of persistence. However, it is important to recognize that the model does not explain all variability in longitudinal measures of E. coli concentrations on Callahan Creek or Roaring Fork ( = 0.29 and 0.24, respectively).

CONCLUSIONS

Although the genetic marker Bacteroides-HF183 has routinely been detected downstream from highly populated areas contaminated by human sewage (Seurinck et al. 2005; Sidhu et al. 2013; Harwood et al. 2014), there has been little previous demonstration of its application to rural areas. This study demonstrated that human-specific Bacteroides-HF183 can be found in rural watersheds with known upstream direct discharges of untreated household waste (e.g. via straight-pipes). However, given its sporadic recovery, it may be most appropriate to use this marker in conjunction with more traditional culture-based FIB following initial sanitary surveys to identify areas most at risk. Used concurrently with E. coli (as has been previously suggested by Harwood et al. (2009) and Sidhu et al. (2013)), human-specific Bacteroides may provide strong evidence of human fecal contamination, and contribute to justification or evaluation of infrastructure improvements. Ideally, epidemiological studies and water quality information at the most common point of exposure (i.e. private drinking water sources) alongside water surveys would allow for more informed decisions on the scale of investment in wastewater infrastructure.

ACKNOWLEDGEMENTS

Funding for this project was provided by the Appalachian Research Initiative for Environmental Science. The views here are those of the authors and do not necessarily represent the views of the sponsoring organization, nor has the sponsoring organization taken any role in the research. Statistical assistance was provided by the Laboratory for Interdisciplinary Statistical Analysis, as well as Karen Kline from the Department of Biological Systems Engineering at Virginia Tech. The Virginia Department of Health provided information on the location of straight pipes and septic systems in the study watersheds.

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