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Transport of fecal indicators from beach sand to the surf zone by recirculating seawater: laboratory experiments and numerical modeling Kendra Irene Brown, and Alexandria B. Boehm Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02534 • Publication Date (Web): 26 Oct 2016 Downloaded from http://pubs.acs.org on November 4, 2016

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Environmental Science & Technology

Transport of fecal indicators from beach sand to the surf zone by recirculating seawater: laboratory experiments and numerical modeling Kendra I. Brown and Alexandria B. Boehm∗ Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305-4020 E-mail: [email protected] Phone: (650) 724-9128. Fax: (650) 725-3164

Abstract

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Recirculating seawater is an important component of submarine groundwater dis-

3

charge, yet its role in transporting microbial contaminants from beach sand to coastal

4

water is unknown. This study investigated the extent to which recirculating seawater

5

carries fecal indicators, Enterococcus and bird-associated Catellicoccus, through the

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beach subsurface. Laboratory experiments and numerical modeling were performed to

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characterize the transport of fecal indicators suspended in seawater through medium-

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grained beach sand under transient and saturated flow conditions. Enterococcus was

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measured both by culture (cENT) and DNA assay (tENT), and Catellicoccus (CAT)

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by DNA assay. There were differences between transport of tENT and CAT com-

11

pared to cENT through laboratory columns containing beach sands. Under transient

12

flow conditions, first-order attachment rate coefficients (katt ) of DNA markers were ∗

To whom correspondence should be addressed

1

ACS Paragon Plus Environment


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greater (∼10h−1 ) than katt of cENT (∼1h−1 ), although under saturated conditions

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katt values were similar (∼1h−1 ). First-order detachment rate coefficients, kdet , of

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DNA markers were greater (∼1h−1 ) than kdet of cENT (∼0.1h−1 ) under both types of

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flow conditions. Incorporating the rate coefficients into field-scale subsurface transport

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simulations showed that, in this sand type, the contribution of recirculating seawater

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to surf zone contamination is likely to be minimal unless bird feces are deposited close

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to the land-sea interface.

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Introduction

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When coastal sand and water are contaminated with human or animal feces, beach visitors

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can become ill from exposure to pathogenic microorganisms. To protect public health,

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USEPA has established recreational water quality criteria which require monitoring for fecal

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contamination. Criteria for marine waters require measurements of culturable Enterococcus

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(cENT). If cENT levels exceed regulatory limits, then a beach advisory is posted. Routine

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monitoring in the United States resulted in 19,457 beach advisories and closures in 2014

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owing to elevated levels of microbial pollutants; 78% of those advisories were due to unknown

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sources of contamination 1 . Because water quality violations not only raise public health

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concerns but also harm local economies 2 , there is a clear need to identify the sources of and

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remedy microbial pollution in coastal waters.

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Fecal contamination at beaches may originate not only from exogenous sources, such

32

as leaking sewer lines or stormwater, but also from endogenous sources. Bird feces are

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common endogenous sources at beaches 3–5 that contain high densities of cENT 6 . Once fecal

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contamination is introduced to the beach, sand and wrack can harbor cENT or even promote

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their growth 7–10 . Several studies have investigated whether cENT might be released from

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the sand reservoir into coastal water via over-beach transport, defined as the mobilization or

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elution of sediment-associated bacteria by seawater via rising tides, wave events, or wave run-

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up and their subsequent transport to coastal waters 11–17 . An additional potential pathway for 2


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the transfer of cENT from sand to the coastal ocean is via submarine groundwater discharge

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(SGD).

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SGD, the flow of coastal groundwater from the beach subsurface into the ocean, is com-

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posed of both meteoric water and recirculating seawater 18 . The latter component has been

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described by several field and modeling studies as subsurface flow that occurs when tides

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and waves cause fluctuations in the hydraulic gradient within the nearshore sand 19–23 . The

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shifting hydraulic gradient results in seawater circulation within the saturated zone of the

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beach, as shown with numerical models based on field salinity and dye measurements 19,20 .

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Despite the growing understanding of SGD flow, the transport of endogenous bacterial

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indicators, henceforth referred to as “indicators”, from beach sand to the surf zone by re-

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circulating seawater has only begun to be studied. Russell et al. 24 introduced the idea of

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the through-beach transport pathway, and found that cENT are readily carried by flowing

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seawater through the unsaturated zone of the beach to the water table. In addition to cENT,

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there are more recently developed DNA-based indicators of fecal pollution, which are enu-

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merated by quantitative polymerase chain reaction (qPCR). Enterococcus species measured

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by a DNA assay, referred to here as tENT, may also be used for monitoring according to

55

new water quality criteria. Other DNA assays target bird-associated fecal bacteria; an as-

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say for Catellicoccus marimammalium (CAT) has been developed as a sensitive and specific

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indicator of bird feces 3,5,25 . C. marimammalium has not yet been grown in culture, so can

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only be measured by DNA assay. CAT measurements have been made at various beaches to

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investigate the impact of bird feces on water quality 3–5,26,27 .

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The goal of this study is to investigate how recirculating seawater contributes to the

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transport of endogenous fecal contamination, as measured by cENT, tENT, and CAT. We

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investigate two distinct pathways that have been described in the literature (Figure 1). One

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pathway describes the transport of indicators from sand to sea via the recirculation cell

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formed by cyclic tidal and wave processes in the saturated zone of the beach 19–21 . In the sec-

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ond pathway, transport is driven by wave uprush events 22,23 which inundate the unsaturated

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zone of the beach. Transport may vary not only between microorganisms, but also between

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culturable cells and the DNA marker of the same microorganism. To date, those variations

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have not been described, because no DNA-based indicators have been characterized in terms

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of their transport behavior in the published literature. Here we quantify differences among

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indicators by combining laboratory column studies with numerical modeling to fit attach-

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ment and detachment parameters for cENT, tENT, and CAT. These parameters are then

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used to estimate through-beach travel distances at a model beach.

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Methods

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Laboratory Column Experiments

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Collection of environmental media. Beach sand, gull feces, and seawater were collected

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at Cowell Beach, Santa Cruz, California, USA (36◦ 57′ 43.0′′ N 122◦ 01′ 26.2′′ W) using sterile

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technique. Numerous gull feces were collected and composited. A 200g sample of sand was

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used to measure grain size distribution 28 . All media were stored at 4◦ C until experimental

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use. Further details are in the SI.

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Transient flow experiments. Transient flow experiments investigated the transport of

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indicators through initially unsaturated sand by a propagating water front caused by wave

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uprush. The method is described in detail by Russell et al. 24 . Experiments were completed

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in duplicate within 6 hours of sand and feces collection. Sand was added incrementally to

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a 30cm long, 2.5cm diameter sterile PVC column and packed by tapping. A second empty

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sterile column was attached to the top of the sand-packed column to serve as a reservoir.

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The two columns were separated by a ball valve. Experiments were performed by adding

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100ml, approximately 1.5 pore volumes, of water to the reservoir with the valve closed. That

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volume had a height of approximately 20cm in the reservoir. The depth of wave uprush on a

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beach depends on wave height and beach geometry 29 . 20cm is near the maximum expected

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depth of uprush at Cowell Beach given its dimensions (slope=0.05 and width=70m) 30 . The

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valve was then opened, so that the water fell onto the sand surface, initially ponding before

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gradually draining through the sand. The water drained due to gravity, and the effluent was

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captured in 10ml aliquots into sterile tubes with a Spectra/Chrom CF-1 fractional collector

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(Sprectum Chromatography, Houston, TX).

95

Packed sand was prepared for experiments by releasing 100ml of filter-sterilized seawater

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through the sand four distinct times to remove any loosely attached background microorgan-

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isms from the sand. The effluent from the final release was collected to assay for background

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indicator concentrations. The concentrations were low: less than 10 CFU cENT/ml, and

99

non-detectable for CAT and tENT.

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For the experiment itself, 10g of sand seeded with gull feces, approximately 2cm deep, was

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placed at the top of the sand column. Next, 100ml of filter-sterilized seawater was released

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through the sand three distinct times. The flow rate depends on the hydraulic gradient,

103

and thus had a maximum at the start of each flush (10.2ml/min in Column 1 and 9ml/min

104

in Column 2), then decreased gradually. A single flush was considered complete when the

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discharge rate from the column slowed to less than 0.1ml/min, at which point approximately

106

98% of the infiltrating volume had been collected as effluent. This procedure simulated the

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effect of individual wave uprush events.

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Saturated flow experiments. The saturated column experiments were performed within

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24 hours of sand and feces collection. Duplicate experimental columns were run in parallel in

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identical sterile polycarbonate columns, 2.5cm inner diameter and 16cm long. Sand was wet-

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packed into filter-sterilized seawater. The columns were aligned vertically, and a peristaltic

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pump flowed water from a single reservoir upward through both columns.

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To remove any mobile cENT, tENT, or CAT initially present in the sand, both columns

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were flushed overnight with filter-sterilized seawater. Thereafter, the column influent was

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switched to seeded seawater. The influent reservoir containing seeded seawater was stirred

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throughout the experiment. Approximately 3.9 and 4.3 pore volumes of seeded seawater were

117

pumped through Columns 1 and 2, respectively. The two columns were attached to the same

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pump but different pump heads, which delivered water at slightly different flow rates (0.85

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and 0.95ml/min, respectively). The pore water velocity (v = q/θ) was 0.37-0.41cm/min,

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within the range of literature values for porous media column studies 31–33 . This rate was

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chosen to be similar to the maximum groundwater discharge rate at Cowell Beach of 2.2m/d

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estimated by Russell et al. 27 . Effluent samples were collected every ten minutes with a

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fraction collector in 8.5 and 9.5ml volume intervals in columns 1 and 2, respectively (≈0.25

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pore volume intervals). The total run time was 200min. The influent concentration of

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indicators was tested at the beginning and end of the experiments.

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Salinity tracer. Following bacterial transport experiments, salinity tracer tests were run

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in duplicate through both the transient and saturated columns. We assume that there are no

128

size or charge effects present, so the dispersion of indicators will be the same as dispersion of

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the salinity tracer 34 . The sand columns were first flushed with deionized (DI) water. For the

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transient column, DI water was released from the reservoir onto the sand column in three

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100ml pulses, followed by a 100ml pulse of filtered-sterilized seawater. For the saturated

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column, three pore volumes of DI water were pumped through. Then, the influent was

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switched to filter-sterilized seawater, which was pumped through the column at a specific

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discharge of 0.17cm/min (10cm/h) until the effluent salinity stopped increasing (slope of

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the salinity versus time curve ≤0.05 for three consecutive samples). Effluent samples were

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collected as described above for each flow condition.

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Measurements. Sand porosity was measured directly; salinity and pH were measured

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with probes. cENT were quantified using EPA Method 1600 35 . CAT 5 and tENT 36 were

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quantified using qPCR, including tests for inhibition. Further details of these measurements

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are in the SI. Indicator concentrations in sand are reported per g wet weight.

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Transport modeling and parameter estimation

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One-dimensional indicator transport was modeled with Equations 1-2, a heterogeneous (two-

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component) form of the advection-dispersion equation. Particles physically attach and de-

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tach from sand grains without reaching equilibrium. Attachment and detachment are de-

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scribed by first-order rate coefficients, katt and kdet . ∂θC ∂S ∂ +ρ = ∂t ∂t ∂x ρ

∂C αq ∂x

−

∂qC − θµliq C ∂x

∂S = θkatt C − ρkdet S − ρµsol S ∂t

(1) (2)

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where θ is saturation [L3 /L3 ], t is time [T], x is distance [L], C is suspended concentration

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[CFU or copies/L3 ], ρ is bulk density [M/L3 ], S is attached concentration [CFU or copies/M],

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α is dispersivity [L], q is specific discharge [L/T], katt is the attachment rate coefficient [1/T],

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kdet is the detachment rate coefficient [1/T], µliq is the decay rate coefficient in seawater [1/T],

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and µsol is the decay rate coefficient on sand [1/T].

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Dispersivity for the transient and saturated experiments, αtra and αsat , respectively,

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were adjusted to fit the model to measured effluent saline concentration. katt and kdet were

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adjusted to fit the model to the measured effluent indicator concentrations. Details are

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provided in the SI.

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According to colloid filtration theory, katt is inversely proportional to pore water veloc-

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ity 37 . Because both experiments were run using relatively high, yet realistic, pore water

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velocities, we expect the fitted katt values to be low, and therefore conservative, estimates.

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katt and kdet were compared between indicators and columns using t-tests. Rate coefficients

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for each indicator were found by taking the average obtained from duplicate columns.

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Bacterial decay rates were taken derived from Brown and Boehm 38 (see SI). Models

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were tested for grid insensitivity by decreasing the node interval by an order of magnitude

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and re-running the parameter estimation. There was no resulting difference in parameter

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estimates. 7



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Transient transport. Transient flow and transport were modeled in HYDRUS 1D soft-

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ware (PC-Progress, Riverside, CA), which numerically solves Richards Equation (Equation

166

S1) for variably saturated flow. The time step was 1s, and the node interval was 3mm. The

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initial and boundary conditions are given in Table S3 and Figure S3.

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Hydraulic conductivity as a function of saturation, K(θ) from Equation S1, was taken

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from the HYDRUS soil catalog. Saturated hydraulic conductivity, Ksat , was adjusted to fit

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measured water flux. The overall fit of the model to the salinity and indicator concentra-

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tion data was assessed by normalizing the RMSE output from HYDRUS by Cmax from the

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particular experiment.

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Saturated transport. Saturated transport was modeled in MATLAB (Natick, MA). For

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saturated, steady flow, θ, α and q from Equations 1-2 are constants. The model was a

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Finite Element scheme with a Galerkin-Chapeau basis function and a Crank-Nicholson time

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approximation. The time step was 6s, and the node interval was 1mm. The initial and

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boundary conditions are given in Equations S2-S4 and Figure S4.

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αsat , katt and kdet were fit by minimizing the RMSE between the model prediction and the

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experimental measurements. Sensitivity analyses were then performed to explore the effect of

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changing parameter values on RMSE; these analyses were used to estimate 95% confidence

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intervals. The overall model fit to the data was assessed by normalizing the RMSE by

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corresponding influent concentrations. For comparison, katt for cENT in saturated flow was

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estimated with the colloid filtration theory correlation equation (Equation S5).

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Field-scale scenario. Values of katt and kdet were applied to investigate whether fecal

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indicators from bird feces on the beach surface can be transported through the beach to the

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sea by SGD via the saturated pathway (Figure 1a). The goal was to find the distance that

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indicators can be transported along a curvilinear subsurface streamline, perpendicular to the

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shoreline, while maintaining high enough concentrations to adversely affect surf zone water

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quality. We used Cowell Beach as a model field site. 8


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First, we derived threshold concentrations of indicators in SGD at the sand/surf zone

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interface using a previously published mass balance surf zone model for the model beach 27 ,

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described in the SI and Figure S5. The threshold concentrations are SGD concentrations

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at the point of discharge required to achieve surf zone concentrations of 110 CFU/100ml or

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1280 cell equivalents (CE)/100ml, the recommended regulatory limits for cENT and tENT,

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respectively 39 . The tENT limit was also applied to CAT, because there is currently no

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recommended limit for CAT. tENT and CAT target copies were converted to CE as described

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in the SI.

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Next, we used the saturated model to simulate through-beach transport of CAT, tENT

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and cENT from gull feces on the beach surface to the point of SGD discharge. The domain

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of the 1-D model was a 5m length of sand in the direction perpendicular to shore. For the

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initial condition, the first 1mm of sand was contaminated with indicators from 1g of gull

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feces; the remaining length of sand had an indicator concentration of zero. Seawater at the

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inlet boundary had an indicator concentration of zero, and at the outlet boundary there was

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a zero concentration gradient. Details of the model setup, initial and boundary conditions

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are given in the SI and Figures S6 and S7.

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The phase-averaged specific discharge rate of seawater recirculating in the nearshore sand,

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1m/d, was taken from the literature 19,40 . We applied αsat and average katt and kdet values

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obtained from saturated model fitting described herein. Because katt and kdet are inversely

209

proportional to pore water velocity, the values estimated in this study (obtained from col-

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umn experiments with high, yet realistic discharge rates) may over-predict the transport of

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indicators at the phase-averaged pore water velocity. We used the decay rate coefficients

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from Brown and Boehm 38 . Because saturated katt and kdet were not found for tENT in this

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study, the rates for CAT were substituted, on the basis that the transient katt and kdet values

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for tENT and CAT were not different. The model input parameters are listed in Table S7.

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The model was run with a time step of 90s and a node interval of 5mm. It was run

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iteratively for different lengths of time to determine the maximum distance that the indicator

9



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pulse could travel and maintain a maximum greater than the threshold concentration.

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Results

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Sand properties. The porosity of the packed sand columns was 0.47±0.02. Median grain

220

diameter was 0.3mm (range of 0.1 to 0.6mm). The pore volumes of the transient and

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saturated columns were approximately 67ml and 40ml, respectively, due to their different

222

lengths. The pH of the filter-sterilized seawater was 7.6.

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QA/QC. All positive and negative controls for the bacterial assays resulted as expected.

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There was no evidence of qPCR inhibition.

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Transient columns. Indicator concentrations in the seeded surface sand were: cENT≈

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104 CFU/g (both columns), tENT≈ 104 (Column 1) and 106 (Column 2) copies/g, and

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CAT≈ 106 (Column 1) and 108 (Column 2) copies/g.

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Inundating the sand with 1.5 pore volumes of seawater resulted in saturated flow condi-

229

tions for approximately half the duration of each flush. Saturated conditions calculated by

230

HYDRUS occurred simultaneously at the top and bottom of the flow column (Figure S8).

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Indicator breakthrough curves (Figure 2) show three peaks, one per seawater flush. Sam-

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ples collected before the first peak consist of filtered seawater that was initially in the pore

233

space. The first peak represents the arrival of indicators transported from the surface of the

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column to the base of the column by the first flush. That flush also distributed indicators

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over the length of the column. On the second and third flushes, the first 4 samples taken

236

after the flush show a detectable, but relatively low concentration of indicators, possibly or-

237

ganisms attached to sand grains within the column that were released from grain surfaces by

238

swelling water films 24 . Each time point represents a 10ml sample; the arrival of second and

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third peaks on the 5th sample taken after the initiation of each flush indicates that almost 1

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pore volume (67ml) passed through the column before the peak arrived. That suggests that 10


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most of the indicators were retained near the surface after each inundation, and a portion

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were transported through the column with each subsequent flush. With the exception of

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the samples taken after the third flush in Column 1, the peak concentrations decreased with

244

each inundation.

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Fitting parameters ± 95% confidence intervals (Table 1) were estimated as follows: Ksat =

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0.032 ± 0.002cm/s (Figure S9), which is within the expected range of 10−4 − 5 × 10−2 for

247

medium sand; αtra = 0.15 ± 0.01cm (Figure S10); average katt = 1.1 ± 0.7h−1 (cENT),

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6.4 ± 3.3h−1 (tENT), and 12.4 ± 7.9h−1 (CAT); average kdet = 0.066 ± 0.037h−1 (cENT),

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1.3 ± 0.9h−1 (tENT), and 1.1 ± 1.0h−1 (CAT).

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The normalized RMSE values for cENT, CAT and tENT were on the order of 10−4 , 10−6 ,

251

and 10−4 , respectively, for Column 1, and 10−5 , 10−8 , and 10−6 , respectively, for Column 2.

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The RMSE for salinity, normalized by the maximum salinity, was 10−4 . These RMSE values

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indicate a good fit between model and data, because the errors are less than 5% of the data

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range.

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Saturated columns. The average influent concentrations, with 95% confidence intervals,

256

were 650±210 CFU cENT/ml, 3.8×103 ±2.0×103 copies tENT/ml, and 4.3×104 ±3.4×103

257

copies CAT/ml. More than 90% of all indicators that flowed into the columns were retained

258

in the sand (Table S8). The saturated model was not fit to the tENT data, because tENT

259

concentrations in the effluent of the replicate columns were too near the lower detection

260

limit to describe the rising slope of the breakthrough curve (Figure S11). cENT and CAT

261

breakthrough curves are shown in Figure 3. Note that because the flow rates were slightly

262

different in the replicate columns (0.85ml/min in Column 1 versus 0.95ml/min in Column

263

2), samples from the two columns collected at the same time were not taken at exactly the

264

same pore volume.

265

cENT breakthrough curves are similar in that they both reach a maximum effluent con-

266

centration of approximately 1% of the influent concentration. There is not a significant

11



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difference between concentrations measured at similar pore volumes between Columns 1 and

268

2 (p > 0.05).

269

CAT effluent concentrations measured at similar pore volumes, after approximately 1.5

270

pore volumes, are different between the two replicate columns (p < 0.05). Column 1 effluent

271

concentrations increase over the duration of the experiment, while Column 2 effluent concen-

272

trations plateau after breakthrough. Nevertheless, the average effluent concentrations from

273

the 2 columns are similar: effluent concentrations from Column 1 are approximately 1% of

274

the influent concentration, while those from Column 2 are approximately 7% the influent

275

concentration. The salinity breakthrough curve is shown in Figure S12.

276

The average parameter estimates and their 95% confidence intervals obtained from the

277

models (Table 1) were as follows: αsat = 0.068 ± 0.007cm; katt = 3.3 ± 0.1h−1 (cENT)

278

and 3.2 ± 0.4h−1 (CAT); kdet = 0.23 ± 0.08h−1 (cENT), and 0.68 ± 0.28h−1 (CAT). The

279

sensitivity analyses used to find the 95% confidence intervals are shown in Figures S13 - S17.

280

katt for cENT, as estimated by the correlation equation for single-collector contact efficiency

281

(Equation S5), was 5.4h−1 , approximately 60% greater than the experimentally fit katt value,

282

indicating a good order-of-magnitude agreement.

283

Field-scale scenario. Figure S18 shows the migration of the CAT pulse through the

284

beach, as a series of concentration profiles at different times. As shown by the 11d concen-

285

tration profile, the maximum of the CAT pulse from 1g of gull feces can travel 1.3m and

286

remain above the concentration threshold. The similar distance for tENT is 0.08m. The

287

maximum of the cENT concentration profile did not exceed the concentration threshold at

288

any distance. Considering a high contamination case, in which the first 1mm of sand was

289

initially contaminated with indicators from 10g of gull feces, these distances are 1.8m for

290

CAT, 0.6m for tENT, and 0.1m for cENT. These values are summarized in Table S9. A

291

sensitivity analysis showed that transport distances decrease with decreasing groundwater

292

specific discharge rate, q (SI and Figure S19).

12


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Discussion

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Bacterial transport through porous media has been well studied over the past several decades 41–43 .

295

The removal of bacteria during saturated flow occurs due to straining, filtration, and decay 37 .

296

During unsaturated flow, air-water interfaces can affect bacterial retention and release 44,45 :

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bacteria may be retained by water film straining as sand saturation decreases 46 , and released

298

into swelling water films as it increases 24 .

299

In the current study, we found average katt values approximately one order of magnitude

300

greater than kdet values. This is true for all indicators, in both saturated and transient

301

experiments. The result of katt > kdet is consistent with those of most previous studies of

302

bacterial transport in saturated porous media 41,43 . It is necessary to account for both kinetic

303

processes; bacteria generally do not reach a state of equilibrium between liquid and solid 42 .

304

The low, but non-zero detachment rates raise the possibility that contaminated sand releases

305

attached indicators to the surf zone over long time periods via SGD 34 . In order for this to

306

occur, there must be flow out of the beach into the surf zone, which is expected to occur

307

with a seaward hydraulic gradient 47 .

308

Average cENT detachment rate coefficients are significantly less than those of the DNA

309

markers in both saturated and transient flow (t-tests, p < 0.05). This suggests that culturable

310

bacteria are more likely than DNA markers to be retained in the sand. Because DNA markers

311

come from not only culturable bacteria, but also non-culturable bacteria and free DNA, the

312

result suggests that non-culturable bacteria and free DNA are released from sand relatively

313

easily. On the other hand, the attachment rate coefficient for cENT is similar to that of

314

CAT under saturated conditions (t-test, p > 0.05), and is significantly less (t-test, p < 0.05)

315

than that of both DNA markers under transient conditions. This might suggest a difference

316

in the affinity of the targets to air-water interfaces. The fact that the transient experiments

317

were saturated for approximately half of each seawater flush may explain the similarity in

318

average katt and kdet for both conditions.

319

To our knowledge, only attachment efficiencies have been published for Enterococcus 13



320

faecalis under saturated conditions, and neither attachment nor detachment has been char-

321

acterized for the DNA markers. Schinner et al. 48 conducted saturated column experiments

322

by flowing water containing E. faecalis through clean quartz sand. The ionic strength and

323

specific discharge of the water were 10mM and 1.7 × 10−4 m/s, respectively, compared to

324

≈700mM and 3 × 10−5 m/s in this study. Using the values the authors reported for spe-

325

cific discharge, porosity, mean grain size, and attachment efficiency, we used Equation S5 to

326

calculate the single-collector contact efficiency, and then katt =4.7h−1 following Yao et al. 49 .

327

This value generally agrees with those found herein, because it is on the same order of mag-

328

nitude. It is less that calculated from Equation S5 (katt =5.4h−1 ) in this study, but greater

329

than the fitted value (katt =3.3h−1 ). We expect katt to be less in a system with either lower

330

ionic strength or greater specific discharge. The slight inconsistency in comparing the re-

331

sults is likely due to the use different methods to estimate katt , and/or the different systems

332

of laboratory versus natural sand. There is still a need to improve understanding of mi-

333

crobial transport, especially in natural environments 43 where interactions between bacterial

334

surface features and organic matter or mineral impurities might alter sand-bacterial affinity

335

compared to laboratory conditions.

336

Limitations. In this study, we investigated the transport behavior of indicators in medium-

337

grained sand from a single beach. At beaches with coarser sand, through-beach transport

338

might contribute to surf zone contamination to a greater extent than observed herein. Such

339

beaches are expected to have greater hydraulic conductivity, and thus greater specific dis-

340

charge. Increasing specific discharge results in increased transport distances, because there

341

is less time for indicators to attach to sand grains as they flow through the system 50 . Fur-

342

thermore, we expect that katt itself would decrease with increasing specific discharge, which

343

would also result in increased transport distances.

344

With regard to modeling the experimental system, the best fits from minimizing RMSE

345

do not, qualitatively, match the data perfectly. The saturated model fits the bacterial break-

14


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Page 15 of 29


346

through data well as it accurately predicts an initial step increase in effluent concentration

347

(the magnitude of which is controlled by katt ) followed by a gradual increase in concentration

348

over time (controlled by kdet ). However, the model often overpredicts the concentrations im-

349

mediately after breakthrough which may suggest that there is more than one flow region 43

350

or that attachment sites on grain surfaces become filled 50 . Thus, first-order processes may

351

be too simple to fully describe indicator transport in our model system. As for the tran-

352

sient flow model, it succeeds in capturing the breakthrough of indicators following the three

353

consecutive seawater flushes, but in several cases it either under- or over-predicts indicator

354

concentrations in the effluent. It is probable that katt and kdet change over the course of a

355

seawater flush, as the conditions in the column change from saturated to unsaturated 51 , and

356

as the flow rate decreases.

357

The fitted rate coefficients in the transient columns showed a large degree of variability.

358

Notably, there was greater between-column variability for the DNA markers than for cENT;

359

the former varied by at least an order of magnitude, while the latter varied at most by

360

a factor of 2. There is considerably less variability in the fitted rate coefficients between

361

saturated columns for both CAT and cENT. It is not clear, therefore, whether the variability

362

is related to the indicators, e.g. differences in surface properties between culturable and

363

non-culturable organisms, or to the greater complexity of the transient flow experiments

364

compared to saturated flow.

365

Because the intertidal zone of the beach is characterized by transient flow conditions, fur-

366

ther studies to investigate bacterial transport under these conditions are warranted. Better

367

understanding of the effect of wave uprush depth on indicator transport would be particu-

368

larly useful. Field measurements of transient indicator transport may be most appropriate

369

for observing natural conditions, but are very difficult to obtain due to heterogeneities in

370

sand and bacterial distribution, as well as the complexity of three-dimensional subsurface

371

flow 17 .

372

Finally, in the field-scale scenario, we only considered the simple case of gull droppings

15



373

contaminating clean beach sand due to circulation of un-contaminated seawater. In real-

374

ity, both the sand and seawater may contain background levels of fecal indicators. Those

375

indicators may contribute to the contamination pulse, increasing the transport distance.

376

Environmental Implications.

377

ENT and CAT from bird feces can be transported through sand via saturated and tran-

378

sient flow. However, a fraction of the bacterial indicators are removed by sand grains and

379

inactivation during transport. Therefore, the through-beach pathway for transport of fecal

380

indicators from sand to sea can be active under some conditions. The field-scale model

381

shows that bird feces would need to be deposited quite close to the point where groundwa-

382

ter eventually discharges in order to contribute enough bacteria via SGD to cause a water

383

quality exceedance of cENT or tENT. Bird feces deposited within ≈ 2 m of the groundwater

384

discharge point could give rise to high CAT concentrations in the surf zone (≈ 103 CE or

385

copies/100 mL).

386

Transient flow experiments suggest that wave uprush can transport and redistribute

387

cENT, tENT, and CAT initially present at the beach surface through the initially unsatu-

388

rated column of sand overlaying the beach aquifer. This may explain why these bacterial

389

indicators have been measured throughout vertical beach sand profiles 24,52 and in pore water

390

of beach aquifers 17 .

391

While this work investigated transport of fecal bacteria in the beach system, transport of

392

indigenous microorganisms between beach compartments (e.g., surface to subsurface sands)

393

may be important in controlling the beach microbiome 53,54 . Further work that explores

394

transport of indigenous microorganisms within the beach system may yield important in-

395

sights into the ecosystem services supplied by beaches.

396

Supporting Information Available

397

Additional information on methods, results, field-scale scenarios, tables and figures refer16


Page 16 of 29

Page 17 of 29


398

enced in the text.

This material is available free of charge via the Internet at http:

399

//pubs.acs.org/.

400

Acknowledgement

401

This research was supported by a grant from the UPS Endowment Fund at Stanford. K.I.B.

402

was supported by a National Science Foundation Graduate Research Fellowship. Sanjay

403

Mohanty, A.R.M.N. Afrooz, and Steven Gorelick provided suggestions that greatly improved

404

the quality of this manuscript.

17



Page 18 of 29

Table 1: Attachment and detachment coefficients estimated by fitting HYDRUS model to transient column data and MATLAB model to saturated column data. C.R.: Confidence Range. katt (h−1 ) Experiment

Target CAT

Transient

tENT cENT CAT

Saturated cENT

kdet (h−1 )

Column Replicate

Value

95% C.R.

Value

95% C.R.

1

24

(7.9, 40)

2.1

(0.15,4.1)

2

1.2

(0.037, 2.3)

0.093

(0.0025,0.19)

1

11

(4.8, 18)

2.5

(0.67, 4.3)

2

1.5

(0.18, 2.7)

0.022

(0.0015, 0.043)

1

1.5

(0.13, 2.8)

0.059

(0.0042, 0.11)

2

0.73

(0.24, 1.2)

0.074

(0.0222,0.13)

1

3.1

(2.8, 3.2)

0.18

(0.058, 0.23)

2

3.3

(3.0, 3.6)

1.2

(0.90, 1.4)

1

3.3

(3.2, 3.4)

0.33

(0.29,0.40)

2

3.2

(3.1, 3.3)

0.13

(0.072, 0.19)

18


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405

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