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Article

Quantitative Microbial Risk Assessment for Private Wells in Flood-Impacted Areas

by
Anna Gitter
1,*,
Diane E. Boellstorff
2,
Kristina D. Mena
1,
Drew M. Gholson
3,
Kelsey J. Pieper
4,
Carlos A. Chavarria
1 and
Terry J. Gentry
5
1
Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center-Houston School of Public Health, Houston, TX 77030, USA
2
Texas A&M AgriLife Extension Service, College Station, TX 77845, USA
3
National Center for Alluvial Aquifer Research (NCAAR), Mississippi State University, Stoneville, MS 38776, USA
4
Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
5
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Water 2023, 15(3), 469; https://doi.org/10.3390/w15030469
Received: 14 December 2022 / Revised: 11 January 2023 / Accepted: 18 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Application of Quantitative Microbial Risk Assessment (QMRA) in Water Safety Management)
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Abstract

:
Microbial contamination of private well systems continues to be a prominent drinking water concern, especially for areas impacted by floodwaters. Hurricane Harvey deposited nearly 60 inches of rain, resulting in extensive flooding throughout Houston, Texas, and neighboring counties. A sampling campaign to test private wells for fecal indicator bacteria was initiated in the weeks following flooding. Escherichia coli concentrations measured in wells were utilized in a quantitative microbial risk assessment to estimate the risk of infection for both drinking water and indirect ingestion exposure scenarios. Derived reference pathogen doses indicated that norovirus (1.60 × 10−4 to 8.32 × 10−5) and Cryptosporidium (2.37–7.80 × 10−6) posed the greatest health risk via drinking, with median health risk estimates exceeding the U.S. Environmental Protection Agency’s modified daily risk threshold of 1 × 10−6 for a gastrointestinal infection. Bathing (1.78 × 10−6), showering (4.32 × 10−7), and food/dish washing (1.79 × 10−6) were also identified to be exposure pathways of health concern. A post-flood microbial risk assessment of private wells in the Gulf Coast has not previously been conducted. Estimating these health risks can provide scientifically supported guidance regarding which well water practices are safest, especially when well water quality is unknown. Developing this guidance is critical as coastal communities experience increased vulnerability to flooding.

1. Introduction

Since the 1950s, the frequency of flooding in coastal areas has dramatically increased due to climate change [1]. Increases in the frequency and magnitude of floodwaters can result in drinking water contamination and the transmission of waterborne diseases [2]. Public water utilities monitor and ensure the safety of their drinking water supplies per the requirements of the United States federal Safe Drinking Water Act (SDWA). However, private wells are exempt from the SDWA. Drinking water supplied by these systems poses an increased health risk for gastrointestinal infections and illnesses for consumers, especially during natural disaster events [3]. Private wells also often lack continuous disinfection, therefore presenting a significant health risk for pathogen exposure during flooding. In response to a flood event, the U.S. Environmental Protection Agency (EPA) advises that well-users should protect the wellhead and its components (such as inspecting the well and pump), disinfect, and sample well water for fecal indicator bacteria (FIB). However, post-flood well water practices are not always implemented [4,5,6]. The lack of awareness regarding well contamination and well stewardship behaviors post-flood can present an increased health risk for those drinking and using the water.
Over 60 inches (1524 mm) of rain fell from Hurricane Harvey resulting in extensive flooding and damage to the City of Houston, TX (USA) and coastal counties [7,8]. Our team predicted that 131,506 to 263,012 private wells were impacted by Hurricane Harvey floodwaters [9]. Traditional techniques to evaluate well water contamination post-flood is to test for FIB, including total coliforms and/or Escherichia coli (E. coli).
Assessing well water for fecal contamination in flooded areas is imperative given that reports of rainfall and flooding affecting groundwater and resulting in gastrointestinal illnesses, while limited, do exist [10,11,12,13,14,15]. Hurricane flooding and acute gastrointestinal illness (AGI) visit rates evaluated in North Carolina identified that flooding was associated with an increase in AGI emergency department visits, especially for American Indians and Black patients [13]. Flood events in Southern Ireland and Canada were all linked to increases in either FIB in well water and/or increases in illnesses associated with waterborne pathogens [10,11,12,16].
The mathematical modeling framework, quantitative microbial risk assessment (QMRA), can be utilized to interpret the health risks associated with exposure. This framework includes four phases—hazard identification, exposure assessment, dose–response assessment, and risk characterization—to estimate the risk of infection following exposure to a microbiological contaminant [17]. There are few QMRA studies specifically examining human health risks associated with private well water [14,18,19,20]. Knowledge gained from QMRA studies can be applied towards risk communication and management practices. Given there is a lack of studies evaluating these health risks associated with private wells, the work presented here can be utilized to inform public health guidance. While private well owners may strive to maintain and protect the quality of their drinking water, well water contamination under flood conditions may still occur.
After Hurricane Harvey, our team showed that there was a 10% increase in samples testing positive for total coliforms and a 7.1% increase in samples testing positive for E. coli. During non-flood conditions, 19.6% and 3.9% of samples tested positive for total coliforms and E. coli, respectively [9]. Despite this widespread contamination, health implications following the storm were not originally evaluated. Using QMRA, we aim to estimate human health risks associated with exposure to microbially contaminated well water and to provide guidance regarding which specific exposure scenarios may pose the greatest health risk.

2. Materials and Methods

2.1. Sample Collection and Microbial Analysis

Sample collection for this study was conducted through the Texas Well Owner Network (TWON), which is an educational training program offered through the Texas A&M AgriLife Extension Service. Using a citizen science approach [21,22], participants were provided instructions regarding how to collect their well water samples for laboratory analysis. Counties with private well users that chose to participate are displayed in Figure 1. Well water samples were collected and returned for analysis from 11 September 2017 to 16 October 2017 (n = 630 sampling kits). Virginia Tech, Louisiana State University, the Texas A&M AgriLife Extension Service through TWON, and Texas A&M AgriLife Extension county agents, coordinated the distribution of sampling kits and sample pick-up for private well users who chose to voluntarily participate. Well users who participated were provided a sampling kit which included instructions for sampling, a sample bottle, and a questionnaire. Samples were collected following a flush of the system (5 min cold water flush from a high flow tap, followed by 1 min cold water flush from kitchen tap) and returned by participants the same day of collection [9]. While well users were instructed to collect water samples from any working tap, only samples collected indoors (n = 451) were included in the risk assessment. A detailed description of the sampling campaign and procedures can be found in Pieper et al. (2021) [9].
Samples were first collected and shipped on ice to personnel at Virginia Tech to be analyzed for E. coli within 24 h. Beginning in October 2017, samples were shipped to Texas A&M University. The IDEXX Colilert 2000 method (Westbrook, MN, USA) was used to enumerate E. coli (detection limit = 1.01 MPN/100 mL). Sample analysis at Virginia Tech included laboratory controls, while sample analysis at Texas A&M did not. Further information regarding microbial water quality analysis and results are described in Pieper et al. (2021) [9].

2.2. QMRA Methodology

The hazard characterization of QMRA aims to identify the pathogen of interest in the specific risk assessment. Often, when only FIB data are available, an estimated dose of a reference pathogen (e.g., norovirus, E. coli O157:H7, etc.) will be developed [23,24,25]. The exposure assessment evaluates the scenario and potential pathogen dose an individual may be exposed to and requires a dose–response model that is appropriate for the pathogen of interest and exposure scenario. Lastly, the risk characterization utilizes the dose calculated during the exposure assessment in the dose–response model to develop an estimated risk of infection.

2.2.1. Exposure Models

The reference pathogens of concern for drinking water used in the risk assessment include Cryptosporidium, Giardia, Campylobacter, norovirus, Salmonella spp. and E. coli O157:H7. Each of these reference pathogens are found in human sewage and are known to be pathogens of concern in recreational and drinking water sources [25,26]. All reference pathogens listed can result in a gastrointestinal infection. Since E. coli concentrations were measured at indoor water sources, no fate or transport of the FIB or reference pathogens were included in the assessment. Minitab® software (Minitab LLC, State College, PA, USA) was used to develop a probability plot for the interval-censored E. coli concentrations. The dataset was fit to the Weibull, lognormal, exponential, loglogistic, and normal distributions using maximum likelihood estimation. The best fitted distribution for this dataset was based upon the Anderson–Darling (A-D) and Kolmogorov–Smirnov (K-S) tests. Different exposure pathways of well water were evaluated: ingestion from drinking, showering, bathing, brushing teeth, washing food and dishes, and toilet flushing (ingestion via aerosols) (Figure 2). Human sewage was the assumed pollution source due to the potential for floodwaters to damage septic systems and transport wastewater. This also provided a conservative approach for this QMRA. Fecal pollution from non-human sources has been identified to have a lower health risk (for GI infection and illness) than human sources [24,27].

2.2.2. Dose Calculation

A dose for each reference pathogen was calculated using the measured E. coli concentrations (Equation (1)). Concentrations of each reference pathogen in raw wastewater were collected from the literature [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42]. Exposure scenarios were calculated for four different age categories: infant to less than two years old, two years old to less than six years old, six years old to less than 16 years old, and adults over the age of 16 years [43]. The indirect ingestion exposure scenarios (showering, toilet flushing, brushing teeth, and food/dish washing) were evaluated for adults. Only bathing was considered for children due to a lack of available child-specific exposure factors. Parameters utilized to calculate the estimated dose are listed in Table 1.
To incorporate variability into the risk assessment, probabilistic distributions were utilized for the parameters as appropriate and if the information was available. The estimated dose for ingestion is described in Equation (1) [24,44].
D o s e R P = C E . c o l i   C E .   c o l i   i n   W W × 100 × C R P   i n   W W × V
where RP refers to reference pathogen; C E .   c o l i is the concentration E. coli measured in well water samples (MPN/100 mL); C E .   c o l i   i n   W W is the concentration of E. coli measured in raw wastewater (CFU/L); C R P   i n   W W is the concentration of the specified reference pathogen in raw wastewater (number of organisms/L); and V is the volume of water ingested (mL).
Our QMRA only evaluates the individual risk of exposure in a static model and does not consider immunity or secondary transmission [45].
Table
Table 1. Input parameters for Monte Carlo simulations regarding ingestion as an exposure scenario.
Table 1. Input parameters for Monte Carlo simulations regarding ingestion as an exposure scenario.
ParametersUnitConcentrationSource
E. coli concentration in well waterlog MPN/100 mL−4.835, 3.824 aEnvironmental data
E. coli concentration in raw wastewaterlog10 CFU/L6.7, 8.0 b[46]
Norovirus concentration in raw wastewaterlog10 copy/L4.7, 1.5 c[28]
Cryptosporidium concentration in raw wastewaterlog10 oocysts/L−0.52, 4.7 b[33,34,37,42,47]
Giardia concentration in raw wastewaterlog10 cysts/L0.51, 4.2 b[32,33,47]
Salmonella spp. concentration in raw wastewaterlog10 CFU/L0.5, 5 b[40,41,47]
E. coli O157:H7 concentration in raw wastewaterlog10 CFU/L−1, 3.3 b[38,47]
Campylobacter concentration in raw wastewaterlog10 MPN/L2.9, 4.6 b[39,47]
Volume of water ingested (L)
Infants to < 20.82 d,e[43]
Children 2 to < 60.76 d,e
Children 6 to < 161.3 d,e
Adult2.5 d,e
Indirect ingestion (mL)Showering0.058, 1.9 f,g[48]
Bathing0.81, 63 h,i[49]
Brushing Teeth1.5 f,j[50]
Toilet Flushing0.01, 0.3 f,k[51,52,53]
Food and dish washing0.007, 0.008, 0.071 l[54]
a lognormal distribution (log mean, log standard deviation); b log10-uniform distribution (minimum, maximum); c log10-normal distribution (mean, standard deviation); d point-estimate (90th percentile); e L/day; f uniform distribution (minimum, maximum); g mL/day assuming one 10 min shower; h gamma distribution (r, λ); i mL/day assuming one bath; j mL per event and assumed to occur twice a day; k assumed 5 flushes per day; l triangle distribution (minimum, likeliest, maximum).
The dose–response equations utilized are based upon feeding studies and outbreak data and include exponential, Beta-Poisson, and Fractional Poisson mathematical models. Feeding and outbreak data for Salmonella spp., Campylobacter jejuni, and E. coli O157:H7 have been fit to a Beta-Poisson dose–response model [17,55,56,57]. An exponential model has been fit to data to estimate the dose–response relationships for Cryptosporidium and Giardia [58,59,60]. Lastly, a Fractional Poisson model has been used to describe the probability of infection for norovirus [61]. The probability of infection for a single exposure event was estimated using the dose–response model for each reference pathogen (Table 2).
A norovirus dose–response model that assumed full particle disaggregation was used as a conservative approach to assessing infection risks [61,63,64]. Untreated drinking water, which is representative of private wells, generally has lower norovirus concentration than recreational waters. Certain models tend to yield higher probability of infection risks but are frequently used in other risk assessments. The other dose relationships presented have all been used in previous water quality-related QMRA studies [24,27,36,65].
The probability of infection due to cumulative daily exposure to indirect routes of water ingestion (showering, bathing, brushing teeth, flushing the toilet, and washing food/dishes) was estimated using Equation (2) [66]. The cumulative daily risk of infection combines statistically independent exposures [24,62,67].
P i n f , d a i l y = 1 − ∏ ( 1 − P i n f , S ) n
where P i n f , d a i l y   is the daily probability of infection from a reference pathogen per each exposure scenario (ingestion: showering, bathing, flushing toilet, brushing teeth and food/dish washing); P i n f , S is the calculated probability of a single exposure for each scenario; and n is the daily exposure frequency.
Crystal Ball Pro® Software (Oracle Corp., Austin, TX, USA) was used to conduct the Monte Carlo simulations (10,000 simulations for each exposure scenario). The QMRA model used input parameters described by statistical distributions (when appropriate) to include inherent variability in the model (Table 1). The daily risks of infection were compared to the modified U.S. EPA risk threshold of 1 infection per 1,000,000 individuals [67,68]. Daily risks were only evaluated. It was assumed that exposure to contaminated water would be short in duration due to the well user testing their water, boiling, using alternative water sources, or disinfecting.

3. Results

3.1. Scenario 1: Drinking Water

The risk of infection for an array of bacterial, protozoan, and viral reference pathogens were assessed to identify which pathogen(s) may pose the greatest health risk when drinking flood-impacted well water. Across all reference pathogens and age groups assessed, the median risk values for norovirus and Cryptosporidium (adult and child subgroups) exceeded the 1 × 10−6 risk of infection threshold (Figure 3). Overall, norovirus appears to have the greatest median risk for infection, compared to the other bacterial and protozoan reference pathogens (Supplementary Table S1). The only parameter that varied between age groups was the daily ingestion volume of water. Adults were assumed to ingest 2.5 L of water daily, which is nearly three times greater than the volume of water assumed to be ingested by infants under the age of 2. Both bacterial pathogens, E. coli O157:H7 and Salmonella spp., had the lowest median health risks. These risk estimates indicate that specific enteric pathogens, such as viruses and protozoa, may be of greater concern than other enteric pathogens in well water. Consequently, these pathogens should be considered an increased health risk during flood events involving septic and wastewater contamination.

3.2. Scenario 2: Indirect Ingestion

The same six reference pathogens were evaluated for indirect ingestion exposures: bathing (children only), showering, flushing the toilet, brushing teeth, and food/dish washing (all considered for adults only). None of the scenarios had a median daily infection risk that exceeded 1 × 10−6. The median risk of a GI infection from norovirus did meet the risk benchmark for bathing (1.78 × 10−6) and food/dish washing (1.79 × 10−6) (Figure 4; Supplementary Table S2). The exposure scenarios that tended to have a greater risk of infection included bathing, showering, and food/dish washing. Toilet flushing and brushing teeth were identified as the exposure pathways with the lowest risk. For Giardia and Salmonella, the 95th percentile risks were four orders of magnitude below the risk benchmark.
The risks associated with each exposure pathway were not consistent for each reference pathogen. For example, the risk of infection from brushing teeth exceeded the risk of infection for showering for Cryptosporidium, Giardia, and norovirus; however, the risk of infection from brushing teeth was lower than the risk from showering for Campylobacter, E. coli O157:H7, and Salmonella. The variability in risks may be due to the very low pathogen doses and the dose–response models utilized [26,63,69]. Given these risk estimates, certain pathways—bathing, showering, and food/dish washing—might need to be avoided if there is any concern that a well may have been damaged or contaminated.

3.3. Sensitivity Analysis

Sensitivity analyses were conducted to evaluate the impact of each model parameter on the health risks. For all six reference pathogens and exposure scenarios, the E. coli distribution in well water parameter contributed the greatest influence and variability on all risk of infection estimates. Additionally, the concentration of E. coli and reference pathogens in raw sewage also influenced the health risks. Other parameters (as described in Table 1) were determined to not be significant contributors to variability in risk estimates. As identified by this sensitivity analysis, the concentration of E. coli, which is used to estimate the pathogen concentration, is the greatest driver of health risk for the specific exposure scenarios presented in this study.

4. Discussion

Hurricane Harvey adversely impacted coastal communities across 41 counties in the Texas Gulf Coast, including nearly 526,000 private well users [9]. This study identified potential health risks for GI infections for private well users who may have experienced well contamination or damage from Hurricane Harvey and its floodwaters. Characterizing these risks is critical to informing public health education and outreach for future natural disasters.

4.1. Well Water Health Risks for Enteric Pathogens

Well water may not be a primary drinking water source during or following a natural disaster event, given the uncertainty of well water contamination. However, indirect exposure via showering, bathing, brushing teeth, food/dish washing, and toilet flushing, could potentially be of concern. Even for several weeks after Hurricane Harvey, E. coli were detected in well water [9]. While bottled water may be an alternative or supplemental drinking source, risks associated with using well water for other uses do exist. Under all exposure scenarios, norovirus was the pathogen of greatest health risk. All estimated health risks for norovirus in drinking water exceeded the daily risk threshold of 1 × 10−6. Similarly, the human health risks associated with exposure to norovirus while bathing and food/dish washing also nearly exceeded the daily risk threshold (1.78 × 10−6 and 1.79 × 10−6, respectively). The bacterial reference pathogen—Campylobacter—and protozoan—Cryptosporidium—could also pose a risk if well water is used for drinking. A study of private wells in Canada also identified Cryptosporidium and norovirus as the predominant pathogens likely causing gastrointestinal illnesses [18]. Several pathogens, including viruses and protozoa, exhibit slower inactivation rates in groundwater sources than FIB (e.g., E. coli, etc.), emphasizing the potential health risks that may exist even if FIB are no longer detected [70,71,72]. Given that these private wells are not monitored under ambient or natural disaster conditions, and that well water treatment is the responsibility of the well user, the health risks for exposure to enteric pathogens are likely underreported. Widespread and affordable testing and disinfection or filtration/treatment protocols should continue to be offered to well users, especially following a natural disaster.

4.2. Indicators for Evaluating Health Risks in Well Water

Indicator organisms are critical for rapidly and cost-effectively assessing well water quality; however, traditional indicators (e.g., total coliforms and E. coli) may not be appropriate for representing all enteric pathogens. Given that both total coliform and E. coli concentrations were elevated in well water samples following Hurricane Harvey, these microbial indicators were useful in identifying environmental contamination of wells, possibly from floodwater (and fecal contamination) [9]. Often, baseline conditions regarding these indicator organisms in private wells are unknown, limiting the knowledge gained from emergency and rapid-response well testing. Numerous factors such as well maintenance, amount of rainfall, climate, season, land use, and geology can potentially influence the likelihood of a well becoming contaminated, whether impacted by floodwater or not [9,15,73,74,75]. The extensive screening and outreach provided by TWON has helped to provide baseline monitoring data of wells across Texas and can assist future work that aims to characterize well contamination under flooding and other environmental factors. However, the utility of E. coli to represent the potential presence of all fecal pathogens, specifically viruses, is limited. Coliphages, a group of viruses that can infect coliform bacteria and serve as viral indicators of fecal pathogens, have been approved for groundwater monitoring by the U.S. EPA. Of 122 wells sampled in North Carolina, total coliforms and E. coli were detected in approximately 20% of samples, while male-specific and somatic coliphages were detected at a higher frequency (66% and 54% of samples, respectively) [76]. Incorporating coliphage testing into well water sampling can improve current knowledge regarding viral pathogens in drinking water sources. Well testing efforts could also include total bacteria counts to help inform the well user of their well water quality and the integrity of their distribution system. Total bacteria counts potentially indicate deteriorating water quality or favorable conditions for microbial growth [77].

4.3. Barriers to Testing

Given the potential health risks associated with microbial contaminants in flood-impacted wells, outreach and well testing during and following disaster events are imperative. However, it is well known that well users may not be able to seek testing or disinfection services due to an array of barriers (including cost, transportation, inconvenience, and lack of access [78,79]). To address cost and transportation barriers in Marquette County, Wisconsin, the health department provided free testing for 150 households and assisted with sample drop-off and shipping [79]. Well testing initiatives in Ontario identified that providing sample bottle pick-up and drop-off and dedicated resources for well water testing can assist with increasing testing participation [78]. Public education regarding the importance of water quality in private wells is critical to facilitate routine testing and increase the initiative for well users to seek testing after natural disaster events. Outreach personnel, such as TWON, are instrumental in identifying and mitigating barriers to private well water testing in Texas.

4.4. Challenges

This study did not distinguish samples from wells that were characterized by specific factors that may have influenced the likelihood of a well becoming contaminated or flooded (e.g., proximity to floodwaters). All samples collected between 11 September and 16 October were included in the risk assessment to provide a multi-exposure pathway characterization of the health risks associated with private wells impacted by Hurricane Harvey. The scope of this risk study, given that samples were collected directly from the faucet and not from floodwaters or well tanks, did not incorporate microbial decay or transport. Refining future approaches that assess pathogen concentrations and risks across time (e.g., risks during flooding and number of days after flooding) can inform emergency response communication, management, and well stewardship post-natural disaster. The QMRA presented in this study applied environmental data to characterize immediate health risks following contamination that resulted from flooding up to two months prior.
The risk assessment utilized input parameters and dose–response relationships that were gathered from the literature and based upon the best available knowledge at the time. The pathogen and indicator concentrations and ingestion volumes for each exposure scenario likely vary among different environments, age groups, and communities. While the assumptions presented in this study incorporated variability as best as possible, the risk of infection estimates should be evaluated as a characterization of health risks unique to these private wells in Texas. Future risk assessments could incorporate indicator or pathogen decay and evaluate how health risks may change over time.
Lastly, characterizing health risks utilizing FIB does present challenges. E. coli is the standard FIB utilized in drinking water testing, but it has been known to regrow and become naturalized in the environment [80]. Further, the specific fecal sources contaminating the well (whether human or non-human) remain unknown unless MST techniques are utilized. However, E. coli testing is relatively rapid and low-cost, which is critical when responding to a natural disaster event. Future work targeting specific MST markers to assess fecal contamination or fecal pathogens for a subset of samples can be informative to assess sources and pathways of contamination; however, given the vast amount of data that was gathered by citizens and researchers, the utility of FIB to assess water quality and health risks should not be disregarded. Additionally, private well systems lack continuous disinfection and have the potential for water to stagnate in the system or for water heaters to not be hot enough, potentially increasing the risk of exposure to Legionella and other opportunistic pathogens. Approximately 15% of wells evaluated following the 2016 Flood in Louisiana had detectable concentrations of L. pneumophila, presenting a potential risk for human health [81].
These findings characterize the health risks for individuals who rely on private wells in flooded areas (or impacted by other natural disasters). Norovirus was identified as the reference pathogen of greatest concern for all exposure scenarios. Cryptosporidium was also associated with elevated health risks in drinking water, while other pathogens, including Campylobacter, could pose a potential health risk via indirect ingestion. Individuals will likely seek alternative drinking water sources, such as bottled water, to mitigate risks associated with drinking water from the tap. However, exposure via indirect ingestion through showering, bathing, and food/dish washing may be overlooked and can be a public health concern. The health risks associated with these other indirect exposure pathways should be communicated to impacted communities. Respiratory infections from opportunistic pathogens are poorly characterized and warrant further exposure and risk analysis. Research evaluating well owner behavior can aid in informing future health risk assessments. Improved characterization of these health risks can assist in providing effective outreach and emergency response measures to private well users.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w15030469/s1, Table S1: Median daily risk of infection for each age group and reference pathogen for drinking water; Table S2: Median daily risk of infection for indirect ingestion exposure scenarios.

Author Contributions

Conceptualization, A.G., D.E.B., D.M.G., T.J.G., K.J.P. and K.D.M.; methodology, A.G., T.J.G. and K.D.M.; formal analysis, A.G.; data curation, A.G.; writing—original draft preparation, A.G.; writing—review and editing, A.G., D.E.B., D.M.G., T.J.G., K.J.P. and K.D.M.; visualization, C.A.C. and A.G.; supervision, T.J.G., D.E.B. and K.D.M.; project administration, D.E.B., D.M.G. and K.J.P.; funding acquisition, D.E.B., D.M.G. and K.J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was also made possible in part by partial support from the National Science Foundation Rapid Response Research (RAPID) Program under Grant Number 1760296. The project was also partially supported through the Federal Emergency Management Agency funding and through the Clean Water Act §319(h) Nonpoint Source funding from the Texas State Soil and Water Conservation Board and the United States Environmental Protection Agency under Grant Numbers 10-04 and 13-08. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Science Foundation, Federal Emergency Management Agency, the Texas State Soil and Water Conservation Board, or the United States Environmental Protection Agency.

Data Availability Statement

Summaries of data presented in this study are available on request from the corresponding author. The data are not publicly available due to maintaining confidentiality for participants.

Acknowledgments

At the time the research was conducted, A.G. was supported by a graduate assistantship and the Mills Scholarship from the Texas Water Resources Institute and Texas A&M AgriLife.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Texas counties with well users that chose to voluntarily participate in the post-flood well water sampling campaigns.
Figure 1. Texas counties with well users that chose to voluntarily participate in the post-flood well water sampling campaigns.
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Figure 2. Exposure pathways for ingestion of well water via drinking and through incidental ingestion.
Figure 2. Exposure pathways for ingestion of well water via drinking and through incidental ingestion.
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Figure 3. Daily risk of a GI infection following ingestion of contaminated drinking water. The red dashed line represents the modified U.S. EPA daily risk threshold (1 × 10−6).
Figure 3. Daily risk of a GI infection following ingestion of contaminated drinking water. The red dashed line represents the modified U.S. EPA daily risk threshold (1 × 10−6).
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Figure 4. Daily risk of a GI infection for indirect ingestion of contaminated well water. The red dashed line represents the modified U.S. EPA daily risk threshold (1 × 10−6).
Figure 4. Daily risk of a GI infection for indirect ingestion of contaminated well water. The red dashed line represents the modified U.S. EPA daily risk threshold (1 × 10−6).
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Table
Table 2. Dose–response models utilized to estimate probability of infection in the QMRA.
Table 2. Dose–response models utilized to estimate probability of infection in the QMRA.
PathogenProbability of InfectionReferences
Salmonella spp.1 − (1 + dose/2884)−0.3126[55,62]
Campylobacter jejuni1 − (1 + (dose/7.59))−0.145[57]
E. coli O157:H71 − (1 + (dose/48.8))−0.248[56]
Cryptosporidium1 − exp(−0.09 × dose)[60]
Giardia1 − exp(−0.01982 × dose)[58,59]
Norovirus0.72 × (1 − exp(−dose/1))[61,63]
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Gitter, A.; Boellstorff, D.E.; Mena, K.D.; Gholson, D.M.; Pieper, K.J.; Chavarria, C.A.; Gentry, T.J. Quantitative Microbial Risk Assessment for Private Wells in Flood-Impacted Areas. Water 2023, 15, 469. https://doi.org/10.3390/w15030469

AMA Style

Gitter A, Boellstorff DE, Mena KD, Gholson DM, Pieper KJ, Chavarria CA, Gentry TJ. Quantitative Microbial Risk Assessment for Private Wells in Flood-Impacted Areas. Water. 2023; 15(3):469. https://doi.org/10.3390/w15030469

Chicago/Turabian Style

Gitter, Anna, Diane E. Boellstorff, Kristina D. Mena, Drew M. Gholson, Kelsey J. Pieper, Carlos A. Chavarria, and Terry J. Gentry. 2023. "Quantitative Microbial Risk Assessment for Private Wells in Flood-Impacted Areas" Water 15, no. 3: 469. https://doi.org/10.3390/w15030469

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