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Characterization of Natural and Affected Environments
Can we swim yet? Systematic review, meta-analysis, and risk assessment of aging sewage in surface waters Alexandria B Boehm, Katherine E Graham, and Wiley Jennings Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01948 • Publication Date (Web): 06 Aug 2018 Downloaded from http://pubs.acs.org on August 6, 2018
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Can we swim yet? Systematic review, meta-analysis, and risk assessment of aging sewage in surface waters Alexandria B. Boehm*, Katherine E. Graham, Wiley C. Jennings Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA 94305 Submitted to Environmental Science & Technology
*
Corresponding author: 473 Via Ortega, Room 189, Dept. of Civil & Environmental Engineering , Stanford University, Stanford, CA 994305.
[email protected], (650) 724-9128 (tel)
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TOC Art
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Abstract
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This study investigated the risk of gastrointestinal illness associated with swimming in surface
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waters with aged sewage contamination. First, a systematic review compiled 333 first order
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decay rate constants (k) for human norovirus and its surrogates feline calicivirus and murine
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norovirus, Salmonella, Campylobacter, Escherichia coli O157:H7, Giardia, and
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Cryptosporidium, and human-associated indicators in surface water. A meta-analysis
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investigated effects of sunlight, temperature, and water matrix on k. There was a relatively large
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number of k for bacterial pathogens and some human-associated indicators (n>40), fewer for
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protozoan and (n=14-22), and few for human norovirus and its Caliciviridae surrogates (n=2-4).
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Average k ranked: Campylobacter>human-associated markers>Salmonella> E. coli O157:H7 >
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norovirus and its surrogates >Giardia>Cryptosporidium. Compiled k values were used in a
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quantitative microbial risk assessment (QMRA) to simulate gastrointestinal illness risk
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associated with swimming in water with aged sewage contamination. The QMRA used human-
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associated fecal indicator HF183 as an index for the amount of sewage present and thereby
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provided insight into how risk relates to HF183 concentrations in surface water. Because
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exposure to norovirus contributed the majority of risk, and HF183 k is greater than norovirus k,
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the risk associated with exposure to a fixed HF183 concentration increases with the age of
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contamination. Swimmer exposure to sewage after it has aged ~3 days results in median risks
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less than 30/1000. A risk-based water quality threshold for HF183 in surface waters that takes
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into account uncertainty in contamination age is derived.
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Introduction. Surface waters, including fresh, estuarine, and marine waters, serve as drinking
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water sources, sites for recreation, sources of food, and essential organism habitat. They are
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susceptible to microbial pollution1–5 from runoff6, animal feces7, and sewage discharges8.
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Microbial pollutants include pathogenic protozoa, viruses, and bacteria. Microbial pollution
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around the world is routinely assessed using concentrations of fecal indicator bacteria (FIB)
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including Escherichia coli and enterococci as proxies for pathogenic organisms9. FIB presence in
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bathing waters is linked quantitatively to gastrointestinal illness risk in swimmers when the FIB
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source is runoff, sewage, or wastewater effluent10–14. However, FIB can be found in a variety of
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sources including animal feces and environmental reservoirs15–17 making them non-ideal
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indicators. Therefore, new indicators that are fecal-source associated have been recently
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developed. For example, human-associated fecal indicators like HF183 and HumM218–21 are
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highly abundant in, and specific to, human feces.
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Once introduced to surface waters, microbial pollutants are transported and dispersed, and
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removed from the water column by a variety of processes including inactivation22. Inactivation
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of microbial pollutants in surface waters can occur via light and dark pathways. Inactivation via
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dark pathways is caused by lack of nutrients, stress, senescence, and exposure to extracellular
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biocidal compounds as well as grazing by bacterivorous organisms22,23. Inactivation can also
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occur via light pathways where photons from the sun induce die-off directly (for example UVB
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damaging genomic DNA) or indirectly via reactive species (generated when photons interact
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with sensitizers like humic acids)24,25.
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Several recent studies have compiled inactivation data on Escherichia coli and enterococci, as
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well as human-associated indicators and pathogens in surface waters26–29. However, a systematic
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screening of the literature on pathogen and human-associated indicators suggested that available
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data are considerably more extensive than reported in these studies. A quantitative synthesis of
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available inactivation data of waterborne pathogens and human indicators would facilitate the
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modeling of these organisms in surface waters by providing a range of relevant decay rate
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constants30. Such data would also be useful in quantitative microbial risk assessment (QMRA)
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scenarios where fecal contamination may be aged31.
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QMRAs have been used extensively to explore how risk of enteric illness varies with bathing
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water exposure to microbial pollution. For example, QMRA was used to show that swimming in
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water containing enterococci from different fecal sources, including gull feces, cow feces, and
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sewage, is associated with different risks32 owing to the fact that different pathogens, with
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different pathogenicity, are present in the different fecal sources. Other bathing water QMRA
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applications allowed calculation of risk-based water quality thresholds for human-33 and gull-
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associated indicators34 for recreational waters; when concentrations of the human- and gull-
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associated indicators are below the risk-based thresholds, then water is safe for swimming. In
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these QMRAs, however, the fecal source was assumed to be “unaged”, meaning that the ratio of
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indicators to pathogens in the fecal source material, an important model input, was assumed to be
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the same as that in the surface water. In reality, once introduced to the environment, the indicator
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and pathogens may decay at different rates, thus altering their ratios.
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In this study, a novel systematic review and meta-analysis of decay rate constants for key
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pathogens and human-associated indicators was conducted. Target-specific decay rate constants
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were summarized as statistical distributions so they may be readily used in a variety of modeling
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applications. We subsequently used the decay rate constants in a QMRA to investigate how
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exposure to fecal contamination from untreated sewage of different ages during swimming
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affects the resultant gastrointestinal illness risk. The QMRA provides a framework for
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identifying a risk-based water quality threshold for the human-associated fecal marker HF183.
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Materials and Methods.
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Systematic review. The systematic review and meta-analysis followed PRISMA guidelines35.
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The goal of the review was to compile from the peer reviewed literature quantitative information
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on the decay of waterborne pathogens and human-associated indicators of fecal pollution in
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surface waters under environmentally-relevant conditions. Pathogens included in the review
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were human norovirus, Campylobacter, Salmonella, Giardia, Cryptosporidium, and E. coli
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O157:H7. Human norovirus surrogates in the Caliciviridae family, feline calicivirus and murine
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norovirus, were also included. Human-associated fecal indicators included were BacHum-UCD,
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HumM2, and HF183. These organisms were included as they were central to our planned QMRA
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application.
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Web of Science core collection (search field = topic), Scopus (search field = article title, abstract,
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keyword), and PubMed (search field = all fields) were searched in August 2017 (Tables 1 and
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S1). The search terms were “(X) AND (water OR seawater OR stormwater) AND (die-off OR
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persistence OR survival OR inactivat* OR decay)” where X is the target-specific text (Table 1).
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Identified articles were assembled and duplicates were removed. Details of the review process,
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which involved two independent full text reviews of papers, are provided in the supporting
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information (SI). The inclusion criteria were the paper: (1) contains quantitative data on the
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decay of the target of interest in raw (unaltered) surface water, (2) is in English, (3) is not a
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review, presents primary data, and is peer reviewed, (4) does not contain data solely on
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disinfection treatments such as addition of oxidants or SODIS, (5) included data from decay
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experiments where the temperature is greater than 4°C and less than 30°C, and (6) describes
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methods to enumerate the target that are logical and justifiable.
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Decay rate constants were extracted from papers by a single reviewer. First-order decay rate
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constants (k), in units per day (d-1), calculated from natural log (ln)-transformed concentration
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data as used in Chick’s law36, were sought. If a study presented k values, then they were
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extracted from the paper along with any reported errors and model fit values (R2 and/or root
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mean square error (RMSE)), and unit conversions were applied where appropriate. If a study
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reported decay model parameters from a model that was not a first-order model (for example a
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shoulder log-linear model, or biphasic model), then we extracted those reported model
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parameters and any associated errors and model fit values. If a study only reported T90 or T99
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values (time to 90% of 99% reduction in concentration, respectively), then they were converted
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to first order decay rate constants assuming Chick’s law applied. If no first order decay rate
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constant was reported by the study authors, then Plot Digitizer
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(http://plotdigitizer.sourceforge.net) was used to digitize the concentration times series appearing
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in graphs within the publication. To be clear, this included data from studies that only reported
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decay model parameters from other types (not first-order log-linear) of decay models. k was then
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calculated as the regression slope of ln(C/Co) versus time (in days) using linear least-squares
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regression in R. In this formulation C is the concentration at time t, and Co is the concentration at
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the start of the experiment at t=0. k and its associated error as well as model fit parameters were
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recorded. In carrying out the linear regression, values reported at or below the detection limit
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were included if and only if they were not preceded by other consecutive values at or below the
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detection limit; the value directly reported by the author was used in these cases.
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Once all data were compiled, datasets and model parameters were examined to assess whether a
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non-linear model was needed to describe decay. The goodness of log-linear model fit to the data
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(R2 and RMSE), and the number of data points that appeared to “deviate” from the log-linear
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model were considered. In general, if R2 values were greater than 0.7 and RMSE was relatively
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small (~1 ln unit), only one data point visually deviated from a straight line fit between time and
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ln(C/Co), or the non-log-linear model fit was no better than the log-linear fit, then a log-linear
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curve fit was deemed acceptable.
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In addition to extracting information on the decay of the target of interest, whether the
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experiment was conducted in (1) freshwater, estuarine water, or seawater and (2) natural sunlight
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or the dark was also noted. If an experiment was reportedly carried out in sunlight, but at a depth
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in the water column greater than ~25 cm or in a container that was opaque to UVA and UVB, the
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experiment was categorized as carried out in the dark. This is justified by previous work
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suggesting that such experiments do not receive sufficient photons to be deemed affected by
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sunlight37. The temperature at which the experiment was conducted was also recorded. If a range
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of temperatures was provided, the mean of the reported range was used. Finally, the method used
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for target enumeration was noted (culture, microscopy, quantitative PCR (QPCR), reverse-
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transcription QPCR (RT-QPCR), or propidium monoazide QPCR (PMA-QPCR)).
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Twenty percent of the papers from which data were extracted by a single reviewer were
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randomly chosen for a second round of data extraction by a different reviewer. Data extracted by
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the two reviewers were compared to ensure consistency. A single author conducted detailed
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review of all datasets to identify missing data, data outliers, and data entry mistakes.
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Meta-analysis. Statistical distributions were fit to target-specific k values. Goodness of fit was
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assessed by visual inspection of residual and Q-Q plots. This yielded log-normal fits for all
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organisms/targets with the number of k values n>14. For congruity, log-normal distributions
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were also used when n
