comparing the partitioning behavior of giardia and cryptosporidium with that of indicator organisms...

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Comparing the partitioning behavior of Giardiaand Cryptosporidium with that of indicatororganisms in stormwater runoff

Adrienne R. Cizeka, Gregory W. Characklisa,*, Leigh-Anne Krometisa,Jeffrey A. Hayesa, Otto D. Simmons, IIIa, Steve Di Lonardob,Kerri A. Alderisiob, Mark D. Sobseya

aDepartment of Environmental Sciences and Engineering, School of Public Health, Rosenau Hall, CB#7431,

University of North Carolina, Chapel Hill, NC 27599-7431, USAbWatershed Water Quality Science and Research Division, New York City Department of

Environmental Protection, USA

a r t i c l e i n f o

Article history:

Received 18 March 2008

Received in revised form

10 June 2008

Accepted 16 June 2008

Published online 1 July 2008

Keywords:

Stormwater

Microbial partitioning

Microbial loading

Settleable solids

Indicator organisms

Cryptosporidium

Giardia

Pathogens

* Corresponding author. Tel.: þ1 919 843 554E-mail address: charack@email.unc.edu (

0043-1354/$ – see front matter ª 2008 Elsevidoi:10.1016/j.watres.2008.06.020

a b s t r a c t

Microbial association with particles can significantly affect the fate and transport char-

acteristics of microbes in aquatic systems as particle-associated organisms will be less

mobile in the environment than their free phase (i.e. unattached) counterparts. As such,

similarities or dissimilarities in the partitioning behavior of indicator organisms and

pathogens may have an impact on the suitability of a particular indicator to act as

a surrogate for a pathogen. This research analyzed the partitioning behavior of two

pathogens (Cryptosporidium, Giardia) and several common indicator organisms (fecal coli-

form, Escherichia coli, Enterococci, Clostridium perfringens spores, and coliphage) in natural

waters under both dry and wet weather conditions. Samples were taken from several

streams in two distinct sampling phases: (i) single grab samples; and (ii) intrastorm

samples obtained throughout the duration of four storms. Partitioning behavior varied by

microbial type, with 15–30% of bacterial indicators (fecal coliform, E. coli, and Enterococci)

associated with settleable particles compared to 50% for C. perfringens spores. Both path-

ogens exhibited similar levels of particle association during dry weather (roughly 30%),

with increased levels observed during wet weather events (Giardia to 60% and Cryptospo-

ridium to 40%). The settling velocities of particle-associated microbes were also estimated,

with those of the bacterial indicators (fecal coliform, E. coli, and Enterococci), as well as

C. perfringens spores, being similar to that of the Giardia and Cryptosporidium, suggesting

these organisms may exhibit similar transport behavior. With respect to intrastorm

analysis, the highest microbial concentrations, in both particle-associated and free phase,

occurred during the earlier stages of a storm. The total loadings of both indicators and

pathogens were also estimated over the course of individual storms.

ª 2008 Elsevier Ltd. All rights reserved.

5; fax: þ1 919 966 7911.G.W. Characklis).er Ltd. All rights reserved.

Notation

CR concentration of microbial or particle type in

raw water sample

CC concentration of microbial or particle type after

centrifugation

CS concentration of settleable microbial or particle

type

SF settleable fraction of microbial or particle type

gC gravitation acceleration experienced by particle

or microbe in centrifuge bottle

S rotor speed, rpm

dC distance traveled during the centrifugation

process

vC velocity of microbial or particle type under

centrifuge conditions

t time of centrifugation (10 min)

vN settling velocity experienced by a microbial or

particle type under normal conditions

gN gravitational acceleration under normal

conditions

vP average settling velocity of a microbe type

associated with settleable particles

vf average settling velocity of free phase microbe of

specified type

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84422

1. Introduction

As of 2004, the USEPA had labeled 38,682 water bodies as

‘‘impaired’’, under Section 303 of the 1972 Clean Water Act

(CWA), with microbial contamination listed as a leading cause

of impairment (NRC, 2001; USEPA, 2007b). Elevated microbial

concentrations in receiving waters following a storm may be

a serious public health concern, as evidence has been found

linking higher levels to disease outbreaks in the United States

(Craun et al., 2005; Curriero et al., 2001; Rose et al., 2001).

Consequently, significant regulatory efforts are currently

being directed toward characterizing and reducing microbial

loadings to receiving waters. These efforts could benefit from

better water quality models, which often serves as the basis

for many regulatory standards, and improved information on

microbial partitioning has the potential to improve modeling

of microbial transport and fate.

Studies have shown that microbes in the water column can

exist as free, unattached organisms or in a particle-associated

form (Characklis et al., 2005; Gannon et al., 1983; Gannon et al.,

1991; Hipsey et al., 2006; Medema et al., 1998). Since the

densities of the organisms themselves are often quite similar

to that of water, the transport behavior of an organism,

particularly its settling velocity, can be significantly affected if

it is associated with a denser and/or larger particle. Previous

studies of microbial–particle interactions, particularly those

involving field samples, have primarily focused on indicator

organisms. While some laboratory-based work has been done

on pathogen–particle partitioning (Feng et al., 2003; Medema

et al., 1998; Searcy et al., 2005), none has involved samples

from natural waters, and each has made use of different

partitioning techniques, making it very difficult to compare

partitioning behavior across different pathogens and/or indi-

cator organisms. This is particularly important since a better

understanding of the partitioning behavior of both indicators

and pathogens would provide one means of assessing the

circumstances under which the former acts as a reasonable

surrogate for the latter.

Better information on microbial partitioning could also

lead to improved modeling predictions that better identify the

timing and location of water quality impairments. Currently,

most microbial modeling efforts assume that all organisms

exist in the free phase, which can lead to less accurate eval-

uations of microbial transport and fate (Ferguson et al., 2003;

Jamieson et al., 2004). Partitioning may be even more signifi-

cant during wet weather, given the dramatic increases in both

microbial and particle concentrations typically observed in

receiving waters following storm events (Hipsey et al., 2006;

Jamieson et al., 2004; Krometis et al., 2007).

This work has several objectives including to: (i) charac-

terize and compare the partitioning behavior of two pathogens,

Cryptosporidium and Giardia with that of several indicator

organisms (fecal coliform, Escherichia coli, Enterococci, Clos-

tridium perfringens, somatic coliphage, male-specific coliphage)

under both wet and dry weather conditions; (ii) estimate how

the loading rates and partitioning behavior of these organisms

vary throughout a storm, as well as the total loadings of path-

ogens and indicator organisms attributable to individual

storms; (iii) estimate the settling velocities of both pathogens

and indicator organisms under wet and dry weather condi-

tions; and (iv) explore correlations between the concentrations

of Giardia and Cryptosporidium and those of the indicator

organisms in several streams. These issues will all be explored

in samples drawn from tributaries feeding the Kensico Reser-

voir, a primary source of water for the city of New York. The

results of this work should provide insights that will be useful

in characterizing microbial behavior in surface waters, while

also providing critical inputs for current modeling efforts

designed to track microbial transport in aquatic systems.

2. Methods

New York City receives most of its drinking water from

reservoirs in the Catskill Mountains and is one of only five

cities in the United States that does not filter its drinking

water, as is typically required by the Surface Water Treatment

Rule (SWTR) (NRC, 2000). As such, the New York City Depart-

ment of Environmental Protection (NYCDEP) has developed an

extensive watershed management plan to protect water

quality in its reservoir system. It has also begun to undertake

modeling exercises to estimate the incidence, transport, and

fate of pathogens that do enter its system. This work is

intended to provide an improved understanding of microbial

partitioning between the particle-associated and free phases,

a potentially important factor in these efforts.

Samples were collected from five different tributaries to

the Kensico Reservoir, with sites labeled as E9, E11, WHIP,

MB-1, and N5-1 (Fig. 1). Sites were selected to represent

contributions from different landuse types within the water-

shed. Site E9 is an unmodified stream draining a large wetland

with little anthropogenic influence, while the watershed

Fig. 1 – Selected sampling sites entering Kensico Reservoir (courtesy of NYCDEP).

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4423

section draining to the WHIP site primarily consists of

suburban woodlots. In contrast, the area upstream from

stream site N5-1 is more developed with a high degree of

impervious surfaces. Both site E11 and site MB-1 also repre-

sent highly developed watershed sub-basins, and samples at

these sites were collected at points where the tributaries

drained into detention basins.

Sampling consisted of two phases. Phase I involved single

grab samples taken under both dry weather and storm

conditions at all five watershed sites, with storm samples

collected as close to the hydrograph peak as possible. These

samples were taken between November 28, 2006 and May 9,

2007 by the NYCDEP using an ISCO sampler, which collected

up to 24 L per sample once it had been triggered near the peak

of the storm hydrograph. Phase II consisted of intrastorm

sampling in which multiple samples were taken throughout

individual storm events at three of the five tributaries

(E9, WHIP, and N5-1) during four storms occurring between

June 4, 2007 and July 12, 2007. Intrastorm sampling consisted

of collection of three separate samples taken from each site,

one during the rising limb of the hydrograph (soon after the

onset of the storm), one near the peak, and one as the

hydrograph receded. The temperatures and pH of all samples

were measured in the field, with samples stored in 24 L sterile

cubitainers. Once the collected samples were sent via over-

night delivery in coolers with ice from New York to the

University of North Carolina at Chapel Hill (UNC).

2.1. Partitioning analysis

Upon arrival at UNC, each 24 L cubitainer was gently inverted

several times to re-suspend material that had settled during

transport. Analysis of indicator organisms (as well as the

physical/chemical analysis) began by removing two 1-L

aliquots from each cubitainer, with one of these set aside for

analysis as the ‘‘raw’’ (unmodified) sample, and the other

reserved for partitioning analysis. Analysis of pathogenic

organisms required somewhat larger volumes, so a 7 L aliquot

was set aside for analysis as the raw sample, and a 10 L sample

was reserved for partitioning analysis.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84424

The approach used to characterize partitioning involves

a calibrated centrifugation technique similar to that described

in several recent studies (Characklis et al., 2005; Fries et al.,

2006; Krometis et al., 2007). The objective of this analysis is to

allow for a distinction to be made between microbes associ-

ated with larger and/or denser particles (henceforth described

as ‘‘settleable’’) and those existing in the free phase or asso-

ciated with smaller less dense particles. The central idea is

that the microbes associated with ‘‘settleable’’ particles will

exhibit significantly different transport behavior in aquatic

systems. The centrifugation approach also represents some-

thing of an improvement over the filtration-based techniques

in that it separates particles and associated microbes on the

basis of both size and density (rather than size alone).

The centrifugation technique was calibrated using latex

beads (density of 1.05 g/cm3, diameters from 5 to 40 mm) as

Fig. 2 – Removal via centrifugation at 500 rpm for (a) polydisper

suspension of latex particles; (c) natural particles from April 15th

site MB-1; and (d) TOC values during the peak of the intrastorm

a surrogate for free phase microorganisms (see top of Table 2

for microbial diameters and densities). Glass beads (density of

2.65 g/cm3, polydisperse mix of diameters from 2 to 20 mm in

diameter) were used as a surrogate for inorganic particles (e.g.

clays, silicates). The centrifugation procedure used in this

work is identical to that described in earlier studies (Char-

acklis et al., 2005; Fries et al., 2006; Krometis et al., 2007) with

the exception of the centrifuge speed, which was reduced due

to concerns over whether a significant fraction of free phase

Cryptosporidium and Giardia, which are slightly larger than

most indicator organisms (AWWA, 1999; Huang and White,

2006), might be removed. As a result, new calibration experi-

ments were performed to determine if a lower rotational

speed would still achieve a high degree of separation between

the glass and latex particle standards. A series of trials

demonstrated that an acceleration of 73g (500 rpm) was

se suspension of glass beads; (b) monodisperse

wet weather sample and April 11th dry weather sample at

sampling event on June 4th.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4425

sufficient to remove >95% of the glass particles (Fig. 2a), while

leaving the vast majority of latex particles in suspension

(density¼ 1.05 g/cm3, monodisperse solutions with diameters

of 5, 10, 20, 43 mm). Roughly 90% of total latex particles

remained suspended, with 98% of 5 mm particles and 77% of

10 mm particles remaining in the supernatant (Fig. 2b).

Results obtained using the centrifugation regime on field

samples from the Kensico tributaries appeared to confirm

that this process was effective in discriminating between

organic and inorganic material, as it removed a substantial

fraction of particles from suspension (Fig. 2c), but only

a very small fraction (roughly 2%) of organic carbon

(Fig. 2d). The fact that a significantly higher fraction of

particles were removed from the storm samples (Fig. 2c) is

interesting and suggests that a higher percentage of denser

(presumably inorganic) particles are mobilized by storm

runoff. It should also be noted that previous work, involving

resuspension of the pellet and a subsequent mass balance,

determined that this centrifugation technique (using accel-

erations up to 1100g) has no significant impact on microbial

survival/culturability or on particle size distribution (Fries

et al., 2006).

Samples were centrifuged at a temperature of 4 �C using

a Sorvall RC-3B centrifuge with an H-6000A rotor with a brake

speed of 4 (approximately 5 min of deceleration time).

Centrifugation involved processing roughly 11 one-liter

aliquots for subsequent microbial analysis (1 L for indicators

and physical/chemical analysis and another 10 L for patho-

gens). Following centrifugation, the top 700 mL of supernatant

was removed from each 1 L centrifuge bottle. In the case of

pathogens, the 700 mL supernatant from each of the 10 one-

liter aliquots were combined for further analysis. The raw

samples and centrifugation supernatant were then indepen-

dently subjected to the same microbial analyses, with the

difference in microbial concentration between the two

defined as the ‘‘settleable fraction’’.

2.2. Physical/chemical analysis

Both the raw sample and the centrifuged supernatant

underwent analysis for total organic carbon (TOC) and particle

number concentration. Particle analysis was performed using

a Coulter Multisizer I electric sensing zone device (Beckman

Coulter, Inc.), with a measurement range of 2–60 mm.

Concentrations of TOC were measured according to Standard

Method 5310B using a Shimadzu TOC-5000 Combustion-

Infrared analyzer (Standard Methods, 1998).

2.3. Microbial analysis

Enterococci in environmental water samples were enumerated

using the Enterolert� Quanti-Tray�/2000 system (IDEXX

Laboratories Inc., Westbrook, Maine), to generate a Most

Probable Number (MPN) value. Samples were similarly tested

for both fecal coliforms (FC) and E. coli using the Colilert�

Quanti-Tray�/2000 system, with positive E. coli cells fluo-

rescing in addition to changing color (indicating a positive

fecal coliform bacteria). Both methods have been shown to be

reliable for detecting and quantifying fecal indicator bacteria

(Bernasconi et al., 2006; Budnick et al., 1996; Pitkanen et al.,

2007). In the case of dry weather samples, undiluted and 10�1

dilutions were used, while undiluted and 10�2 dilutions were

used for storm samples based on previous experience (Kro-

metis et al., 2007). Enterolert� Quanti-Trays�/2000 were

incubated at 41 �C for 24� 3 h (Simmons et al., 2003; Yakub

et al., 2002). Colilert� Quanti-Trays�/2000 were incubated at

37 �C for 2 h to revive environmentally damaged bacteria and

then moved to 44.5 �C for the remainder of the 24� 3 h incu-

bation period to allow for selection of thermotolerant coli-

forms (Chihara et al., 2004; Yakub et al., 2002). Visual

inspection was used to enumerate Quanti-Trays�/2000 post-

incubation for fecal coliform bacteria (color change) and E. coli

were enumerated using a 6 W, 365 nm ultraviolet light for

determining fluorescence wells. Results for both are

expressed in terms of MPN per 100 mL.

Coliphage were quantified using the Single Agar Layer (SAL)

method (USEPA, 2001), a method shown to be reliable for

detecting and quantifying somatic and male-specific coliphage

(Fþ coliphage) in samples up to 100 mL (Sobsey et al., 2004).

These 100 mL samples were separately inoculated with either

log-phase antibiotic-resistant host-culture strains E. coli CN-13

(resistant to nalidixic acid) or E. coli F-amp (resistant to strep-

tomycin and ampicillin) to detect somatic and Fþ coliphages,

respectively. An equal amount of double strength Tryptic Soy

Agar (2� TSA) was added and each sample was poured equally

into four 150 mm� 15 mm Petri dishes. Inverted plates were

incubated for 16–24 h at 36 �C and phages were enumerated

visually by inspecting plates for clear zones of lyses (plaques).

C. perfringens spores were detected using the three-tube

MPN iron milk medium (IMM) method (AOAC, 1995). Samples

were heat-shocked at 70 �C for 20 min to inactivate vegetative

cells, allowed to cool, and added to test tubes containing IMM.

Tubes were then incubated at 41 �C for 18–24 h and observed

for stormy fermentation.

Pathogen analysis for Cryptosporidium and Giardia (oo)cysts

was performed on both the raw sample and the supernatant

using EPA Method 1623 (USEPA, 2007a). Internal standards

were used with all samples (ColorSeed, BTF Pty Ltd, Sydney,

Australia) (Francy et al., 2004). Parasites were recovered by

filtering samples through an Envirochek HV Capsule filter

(Pall Corporation, East Hills, New York) with an absolute pore

size of 1 mm using a pump and 3/800 ID Nalgene tubing.

Parasites were eluted and recovered from the filters by two

successive 100 mL volumes using wrist-action shaking,

which resulted in a total volume of roughly 200 ml per

sample. Each sample was centrifuged at 1817g (2500 rpm)

with samples maintained at 4 �C and with a brake speed of 4.

The bottom 5 mL (including solid pellet) was transferred to

a borosilicate Leighton-type tube (Bellco Glass Inc, Vineland,

NJ) for immunomagnetic separation (IMS) using Dynabeads�

GC-Combo (Invitrogen Corporation, Carlsbad, California). Any

single sample with a pellet of more than 0.5 mL was diluted

to an appropriate volume and split equally so that the portion

of the pellet in each tube was �0.5 mL. Following IMS,

samples were transferred to Merifluor slides (Meridian

Biosciences Inc, Cincinnati, Ohio), stained with Aqua-Glo� G/

C epifluorescent stain (Waterborne Inc, New Orleans, Loui-

siana), and enumerated via immunofluorescent microscopy

at 25� magnification. Although recovery of the internal

standard was low, it primarily fell within the acceptable

Fig. 4 – Assumed scenario involving three particles of

equal size and density settling through the seven sections.

The settleable fraction of a particle/microbe corresponds to

an average distance traveled.

Fig. 3 – Sampling times (rising limb, peak, receding limb) compared with fecal coliform, E. coli, and Enterococci concentrations

for Storm 1 at sampling site N5-1.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84426

range (10–90%) and was used to determine the extent to

which undefined matrix parameters effected method

recovery.

2.4. Estimating total microbial loadings

Sampling was also conducted to allow for an inspection of how

the concentration and settleable fraction of each parameter

(eight microbes, particle number concentration and TOC) varied

throughout the duration of individual storms. Samples were

taken over the course of four storms at three sites, and this led to

thedevelopment of anumber of profilessimilar to that inFig. 3. In

order tomorequantitativelyevaluate thesetrendsoverall storms

and at all sites, each storm was described in terms of three

separatehydrographstages: (1) therising limb–theperiodof time

from the point at which the hydrograph flow begins to increase to

1 h before peak flow; (2) peak – 1 h before the maximum flow was

recorded until 1 h after; and (3) recession – 1 h after the peak flow

value until flow returns to baseline levels.

In order to calculate cumulative storm loadings, the

concentration of each parameter was estimated at points

throughout the storm by linearly interpolating between

measured concentrations using a method similar to that pre-

sented in Krometis et al. (2007). Average dry weather concen-

trations were taken as representative of the concentrations

existing at the beginning and end of the storm (e.g. when

stream flow first increases and when it returns to baseline

levels). Concentration values were estimated at 10 min inter-

vals and all (both estimated and measured concentrations)

were then combined with information on flow rates to esti-

mate loadings for each time interval over the course of the

storm These quantities were then summed to generate

cumulative loading estimates for each parameter over the

entire storm. It is important to note that high variability in flow

and water quality parameters throughout storm events is well-

documented (Characklis and Wiesner, 1997; Wang et al., 2004;

Krometis et al., 2007) and so fluctuations in these values

between sampling points would be anticipated. While linear

interpolation between single grab samples is only a crude

approximation of this variability, it does provide a useful first

order estimate of storm loadings for comparative purposes.

Table 1 – Average concentration of microbial and physical parameters in grab samples: n [ 4 for ‘‘wet’’ weather (i.e. storms n [ 3 for ‘‘dry’’ weather

Site FC (CFU/100 mL)

E. coli (CFU/100 mL)

Enterococci(CFU/100 mL)

C. perfringens(MPN/100 mL)

Fþ coliphage(PFU/100 mL)

Som. coliphage(PFU/100 mL)

Giardia(#/100 mL)

Cryptosporidium(#/L)

Particle conc.a

(# 1000/100 mL)pH

E9

Dry Rawb 319� 542 50� 63 38� 27 48� 42 1� 1 10� 6 2.7� 4.6 2.9� 5.0 1487� 150 7.7� 0.4

Centrifugedc 70� 66 32� 26 14� 6 12� 10 5� 8 18� 20 1.0� 1.8 3.0� 5.2 1332� 289

Wet Rawb 527� 735 161� 71 276� 185 196� 201 3� 5 4� 3 3.5� 4.4 4.4� 5.9 4109� 3 7.2� 0.2

Centrifugedc 553� 542 113� 55 180� 157 338� 520 5� 8 6� 6 1.3� 2.3 1.7� 2.9 2140� 3

E11

Dry Rawb 69� 48 58� 57 12� 9 14� 25 7� 11 11� 4 0.8� 0.7 3.0� 3.2 1289� 172 8.5� 1.0

Centrifugedc 76� 63 55� 66 6� 5 8� 13 14� 17 16� 19 1.4� 1.8 1.2� 1.2 1043� 588

Wet Rawb 2398� 3858 2320� 3916 1322� 1340 160� 109 130� 258 9� 13 5.1� 6.1 7.4� 7.5 6154� 3 7.5� 0.3

Centrifugedc 2078� 2344 138� 78 732� 763 178� 218 112� 219 9� 10 0.8� 1.0 1.3� 1.0 2315� 1

WHIP

Dry Rawb 24� 6 20� 10 11� 9 9� 12 2� 3 12� 10 bt bt 506� 77 8.0� 0.5

Centrifugedc 14� 6 14� 6 12� 16 25� 43 11� 18 16� 14 bt bt 399� 96

Wet Rawb 305� 267 179� 138 372� 339 232� 260 0� 1 3� 1 7.3� 7.5 6.0� 6.4 8385� 6 7.3� 0.4

Centrifugedc 230� 168 105� 89 403� 508 77� 120 2� 3 7� 6 1.5� 1.3 3.8� 2.6 2131� 1

MB-1

Dry Rawb 60� 46 41� 49 31� 37 125� 103 4� 6 7� 6 bt bt 1028� 242 7.6� 0.3

Centrifugedc 69� 34 51� 26 27� 21 34� 51 3� 5 3� 3 bt bt 718� 289

Wet Rawb 1126� 915 608� 475 1458� 1357 506� 451 14� 22 25� 44 1.8� 1.7 1.7� 2.9 10,154� 6 7.3� 0.2

Centrifugedc 1086� 1468 452� 319 574� 408 200� 197 19� 26 26� 35 0.7� 0.7 0.9� 0.9 2838� 1

N5-1

Dry Rawb 113� 120 80� 45 132� 206 255� 194 2� 2 7� 8 3.6� 6.2 3.8� 6.6 1273� 205 7.7� 0.3

Centrifugedc 161� 169 74� 67 140� 216 131� 109 3� 4 6� 6 5.1� 8.9 4.8� 8.4 768� 46

Wet Rawb 955� 707 485� 425 1923� 1201 610� 574 1� 1 9� 7 1.0� 1.6 bt 17,682� 29 7.4� 0.1

Centrifugedc 1179� 922 491� 393 1696� 1496 401� 510 1� 2 9� 12 0.8� 1.0 1.3� 1.6 4276� 2

Note: values shown as mean� 95% confidence interval; bt¼ below threshold.

a Measurable size range¼ 2–60 mm.

b Raw sample is collected sample; no physical treatment.

c Concentration in the supernatant of centrifuged raw sample.

wa

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es

ea

rc

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84

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);

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84428

Cumulative storm loadings were also compared with loadings

for an equivalent period of dry weather flow in order to provide

some basis for comparing the relative contributions of indi-

vidual storms. Dry weather loadings were estimated by multi-

plying average dry weather concentrations by average dry

weather flow and summing these over the length of each storm

(to normalize dry and wet weather loadings for an equivalent

period of time). Subsequently, the cumulative storm loading

was divided by dry weather loading to yield information on the

relative magnitude of microbial contributions to the stream

under both types of condition.

2.5. Correlations between pathogens and indicatororganisms

Data for both the total and settleable concentrations of each

organism were subjected to the nonparametric Spearman

rank statistical test (SAS Statistical Software, V. 9.1) to identify

correlations between the presence of indicator organisms and

that of the two pathogens, Giardia and Cryptosporidium. The

output is described as Rs which falls between �1 and þ1 (with

�1 as a perfectly negative correlation and þ1 as a perfectly

positive correlation).

2.6. Estimating microbial settling velocities

The settling velocities of both microbes and particles were

estimated using an approach based on several assumptions,

including (i) that the sample was perfectly mixed prior to

centrifugation; (ii) that all organisms of a specific type (fecal

coliforms, E. coli, etc.) are of a single size and density; and

(iii) that each settleable particle is of the same density. This

approach generates estimates representative of the average

settling velocity of the settleable particles or microbes of

a specified type, allowing for comparison to those which are

non-settleable (or free phase).

Fig. 5 – Settleable fraction of contaminants during both single gr

weather events for all of the sample sites.

Through earlier analyses the concentration of particles and

microbes in both the raw and centrifuged samples are known

values (CR and CC, respectively). From these values the

settleable concentration of each (CS) is calculated, such that

CS ¼ CR � CC (1)

And the settleable fraction (SF) of each is expressed as

SF ¼ CS

CR(2)

Conceptually, the supernatant is broken down into seven

sections, with the bottom 300 mL described as the ‘‘pellet’’

(Fig. 4). Before centrifugation, all the sections are assumed to

have an equal number of each particle or microbe type (e.g.

sample is perfectly mixed). After centrifugation, some parti-

cles/microbes will have moved out of the original section in

which they resided to lower sections, and some may have

settled into the ‘‘pellet’’ in the bottom 300 mL. The distance

the particle/microbe moved from its original section is deter-

mined by its density and diameter, and the gravitational

acceleration experienced during centrifugation. The gravita-

tional acceleration experienced varies throughout the centri-

fuge bottle, as particles or microbes in Section 1 experience

the least amount of acceleration due to the shorter radial

distance from the center of the centrifuge (which is the

measured 73g described in Section 2), and particles/microbes

in Section 7 experience the greatest amount of acceleration,

due to the longer radial distance from the center. The gravi-

tational acceleration experienced by a particle/microbe during

centrifugation ( gC) is described as

gC ¼ 1:118� 10�5S2ð36:6� dCÞ (3)

where, S¼ rotor speed in rpm (in this case, 500 rpm); dC¼ dis-

tance particle or microbe traveled during centrifugation, cm.

For example, a settleable particle originally residing in

Section 3 (Fig. 4) may travel through three sections during

ab sampling and intrastorm sampling for both dry and wet

Fig. 6 – (a–f) Intrastorm trends in the stormwater concentration. (g–j) Intrastorm trends in the stormwater concentration.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4429

centrifugation, and end up in Section 6. A settleable particle

with the same density and size originally in Section 4 would

experience a greater gravitational acceleration, such that it

might travel a distance equal to four sections, ending up in the

pellet. Any particle of the same size and density found in

sections lower than Section 4 would also settle into the pellet

since they experience an even greater gravitational

acceleration. This conceptual framework, combined with

information on the concentrations of the microbes and

particles, is used to estimate settling velocities for each.

After centrifugation, the supernatant (top 700 mL) is

collected and re-mixed, and all the remaining particles and

microbes in the supernatant are again assumed to be evenly

distributed amongst the seven sections. If settling has

Fig. 6 – (continued).

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84430

occurred, a reduction in particles or microbes will be observed.

The height of the supernatant in the centrifuge bottle is

10.5 cm, so a neutrally buoyant particle (or microbe) would

presumably travel no distance as a result of centrifugation

(hence the settleable fraction would be 0), while for a particle or

microbe of sufficient size and density to travel a distance of at

least 10.5 cm, the settleable fraction would be 1. For any particle

and/or microbe with a size and/or density in between, the

fraction removed from the supernatant is considered directly

proportional to the average distance traveled by that species

during centrifugation. Therefore, every percent of a particle

type removed by centrifugation corresponds to an average

distance traveled during centrifugation (dC) of 0.105 cm (Fig. 4),

the average settling velocity during centrifugation is

vC ¼dC

t(4)

where vC¼ settling velocity of a particle type; t¼ time of

centrifugation.

The distance calculated represents the average distance

traveled for 10 min under the strong acceleration forces that

exist in the centrifuge, but Stokes Law, the mathematical

relationship used to describe settling velocities, is typically used

under normal levels of gravitational acceleration, therefore,

some correction must be made. This is relatively straightfor-

ward, as long as the Reynolds number for the particles is less

than 1 (as is the case for all particles considered here), with the

settling velocity under normal conditions (vN) being propor-

tional to the ratio of the two accelerations, such that

vN ¼ vCgN

gC(5)

where gN¼ gravitational acceleration under normal conditions.

This approach (5) allowed for the calculation of an average

settling velocity for all the microbes (of the specified type) in

the water column, both settleable and non-settleable (i.e. free

phase and particle associated). Free phase settling velocities

(vf) were calculated via Stokes Law using microbial densities

and diameters derived from the literature (see top of Table 2).

This value was then combined with information on the

average overall settling velocity (5) and the settleable fraction

of each microbe (SF) to compute the average settling velocity

of particle-associated microbes (vp), such that

Fig. 7 – (a–f) Intrastorm trends in the settleable fraction of various parameters. (g–j) Intrastorm trends in the settleable

fraction of various parameters.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4431

vP ¼vN � ð1� SFÞvf

SF(6)

3. Results and discussion

Results are discussed within the context of the four objectives

described earlier, first addressing the issue of pathogen/

indicator partitioning and loading, then moving on to esti-

mates of settling velocity and an exploration of correlations

between indicator and pathogen presence.

3.1. Microbial concentrations and partitioning

While there is significant variability within both the dry and

the wet weather periods, the average concentration of

Fig. 7 – (continued).

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84432

particles and all indicator organisms (with the exception of

coliphage) increased considerably as a result of a storm at all

sites (Table 1). Concentrations of Giardia and Cryptosporidium,

while low, also show increases during storm events at all sites

except N5-1. In addition, concentrations of indicator bacteria

(fecal coliform, E. coli, Enterococci) show the largest storm-

related increases at sites E11, WHIP, and MB-1, the same

tributaries that exhibit the greatest increase in Cryptosporidium

and Giardia concentrations.

Results suggest that each of the microbes analyzed is

associated with settleable particles to some degree, although

the degree varies by organism (Fig. 5). The combination of

natural variability in the sampled streams and the analytical

uncertainty inherent in the microbial analysis lead to signifi-

cant variability in the values for the settleable fraction, partic-

ularly for organisms that register relatively low concentrations.

Similar variations have been observed in previous partitioning

studies (Characklis et al., 2005; Fries et al., 2006; Krometis et al.,

2007), thus the analysis of these results focuses primarily on

the mean values of the settleable fraction. In the case of the

bacterial indicators (fecal coliforms, E. coli, Enterococci), the

mean settleable fraction is relatively consistent across all three

organisms (15–30%), under both dry and wet weather condi-

tions. C. perfringens spores (a potential pathogen indicator) had

the highest mean settleable fraction amongst the indicator

organisms, approximately 50%, a value that remains roughly

the same during both dry and wet weather periods. Coliphage

exhibited the lowest average level of particle association,

especially during wet weather periods. It should be noted that

these results are consistent with those in earlier partitioning

studies conducted in stormwater impacted streams in North

Carolina (Characklis et al., 2005; Krometis et al., 2007)

With respect to pathogens, both Cryptosporidium and Giar-

dia exhibit evidence of similar levels of particle association

during dry weather (a mean settleable fraction of roughly

30%). Cryptosporidium partitioning increased slightly during

storms, but there was a more significant increase in the mean

settleable fraction of Giardia during wet weather, up to roughly

60%. With respect to the particles themselves, the settleable

fraction is considerably higher during wet weather, increasing

from roughly 25% in dry weather to 50% during a storm, sug-

gesting that larger and/or denser particles are mobilized by

stormwater runoff (see also Fig. 2c). The settleable fraction of

particles during storm events is more variable than during dry

weather, a factor that may contribute to variability in the

settleable fraction of the microbes. While results in some

earlier studies have suggested that higher concentrations of

both particles and microbes observed during wet weather may

give rise to higher levels of microbe–particle association

(Characklis et al., 2005), there is insufficient evidence in this

Fig. 8 – (a–c) Total stormwater loadings in terms of equivalent periods of background loading.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4433

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 84434

case to make any conclusive statements on this issue. It is also

worth noting that the settleable fraction of total organic

carbon (TOC) is quite low (Fig. 2d), confirming that the vast

majority of particles removed via the centrifugation regimen

are inorganic, and that most organic matter (e.g. organic

particles, free phase microbes) remains in suspension

following centrifugation.

3.2. Intrastorm sampling

Visual inspection of the concentration profiles of individual

storms at each site (Fig. 3) suggested some trends, most

notably that microbial and particle concentrations were

generally higher during the rising limb of the hydrograph,

providing some indication of a ‘‘first flush’’ phenomenon. This

is consistent with intrastorm microbial and particle concen-

trations observed in previous studies (Krometis et al., 2007;

Characklis and Wiesner, 1997). However, in general, concen-

tration levels and partitioning behavior were highly variable

for each microorganism type, both within and across sampling

sites. As a result of this variability, concentrations of each

parameter were averaged by respective storm stage over all

sites and all storms to identify general trends (Fig. 6a–j). These

data indicate that concentrations during the rising limb of

storms were elevated relative to the peak and receding stages

for every parameter (microbial and physical/chemical), with

the exception of C. perfringens spores and somatic coliphage.

The average settleable fraction of each of the 10 parame-

ters was also determined for each storm stage (Fig. 7a–j). The

average settleable fraction of fecal coliform, E. coli, Entero-

coccus, male-specific coliphage and Cryptosporidium all

remained relatively constant throughout the duration of each

storm, a finding consistent with that observed by Krometis

et al. (2007) in a North Carolina watershed. In the case of

Giardia (7a), the average settleable fraction remained at

approximately 0.6 during both the rising and peak stages, but

then declined to roughly 0.1 in the receding stage. The average

settleable fraction of C. perfringens spores (7f) was relatively

constant over the last two stages (around 0.6), but was

significantly lower in the rising limb (around 0.3).

In terms of the loading rate during a storm, it is important to

note that the average concentrations of most of the microbes,

as well as that of the particles and organic carbon, were highest

in the rising and peak stages of a storm (when flow is also at its

highest). So, even though the settleable fraction of most of

these parameters remained constant over the storm’s dura-

tion, the loading rate of settleable material (microbial or

otherwise) was generally highest in a storm’s early stages.

The cumulative loading of microbes entering these

streams as a result of a storm is another measure of storm-

water impact. Microbial loadings associated with wet weather

are, in most cases, many times higher than those experienced

during dry weather conditions. Substantial storm-related

increases in the relative loadings of almost all microbial types

(except Fþ coliphage where increases were only modest),

particles, and organic carbon suggest that stormwater runoff

is the primary contributor of microbial loading to these

streams. In some cases, wet weather loadings were over

10,000 times that of dry weather loadings over an equivalent

period (Fig. 8a–c). As most storm hydrographs represented

a period of one or two days this indicates that a single day’s

worth of storm loading can be the equivalent of several months,

or even years’ worth of dry weather inputs. In the case of the

pathogens, for which such loadings have not been previously

estimated, storm-related increases were not as sharp, but

a single storm’s cumulative loading could still represent as

much as several months of dry weather loading. These results

suggest that water quality improvement efforts in the Kensico

system would be most productively focused on reducing

stormwater-related inputs, as opposed to focusing on contri-

butions from dry weather sources (e.g. leaking septic tanks).

The fraction of cumulative microbial loading associated

with settleable particles was estimated in a similar manner to

that of total microbial loading, with measured values for the

concentration of settleable microbes used to interpolate the

settleable concentrations at unmeasured points throughout

the storm. These values for cumulative settleable loadings

were then divided by the cumulative loading estimates of the

total concentration (settleable and suspended) to generate

estimates of the fraction of cumulative microbial loading that

was settleable (Fig. 9), information that provides some insight

regarding the fraction of microbial loading that will be least

mobile in an aqueous system. This information also offers

some indication of the potential effectiveness of sedimenta-

tion-based stormwater best management practices (BMP) (e.g.

detention basins).

While the fraction of settleable microbial loading across

storms was variable for many microbes, even at the same

sampling site, some general trends emerged. With regard to

the pathogens, the fraction of cumulative settleable loading

for Giardia and Cryptosporidium was reasonably consistent at

around 20% and 5%, respectively. The bacterial indicators

showed results similar to those analyzed in the grab sampling

phase, exhibiting a relatively consistent cumulative settleable

fraction of 20–30%. C. perfringens spores showed the highest

cumulative settleable fraction (80%), but also had significant

variability, while that of the viral indicators (Fþ and somatic

coliphage) was only around 10%. The results for indicator

organisms were similar to those observed by Krometis et al.

(2007) in the stormwater of two urbanizing watersheds in

North Carolina.

3.3. Settling velocities

Differences between the mean microbial settling velocity

values for the settleable fraction estimated in this work and

those estimated for free phase organisms (via previous

experimental studies, or through Stokes Law) provide addi-

tional evidence that each organism exhibits some degree of

particle association (Table 2). Additional support for this

comes from observations that the range of settling velocities

for the settleable fraction of most microbes and particles,

specifically particles between 2 and 10 mm in diameter (where

the majority of particle surface area resides), fall within the

same range (0.47–1.55 mm/s). It is also interesting to note that

the range of settling velocities estimated for the settleable

fraction of the bacterial indicators (with the exception of fecal

coliforms under wet weather conditions), and C. perfringens

spores, are quite similar to that of the pathogens. This would

Fig. 9 – Average fraction of cumulative microbial loadings associated with settleable particles.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4435

suggest that the particle-associated fraction of these organ-

isms should exhibit similar transport behavior.

3.4. Correlations between pathogen and indicatorconcentrations

Despite similarities in estimated settling velocity, there does

not appear to be any indicator organism that consistently

exhibits a strong one-to-one relationship with Giardia or

Cryptosporidium in terms of either total concentration or the

settleable fraction of the organisms in the Kensico system.

When analyzing the single grab sample data alone, weak

correlations exist between the total concentrations of fecal

coliform and that of Giardia and Cryptosporidium (Rs of 0.167

and 0.231 with p-values of 0.35 and 0.19, respectively, indi-

cating confidence levels around 65 and 81%). Stronger corre-

lations exist between the settleable fractions of fecal coliform

and the pathogens (Rs of 0.45 and 0.56 with p-values of 0.22

and 0.10, respectively). When grab sampling data was

combined with the intrastorm data, E. coli and Enterococci

appear to be the best overall indicators of Giardia presence,

although they exhibit only a relatively weak positive correla-

tion with the total concentrations of Giardia, (Rs of 0.12 for total

E. coli and 0.17 for total Enterococci, p-vales of 0.35 and 0.19,

respectively). C. perfringens spores and Fþ coliphage appear to

be the best indicators for Cryptosporidium presence (Rs of 0.13

for C. perfringens and 0.11 for Fþ coliphages, with p-values of

0.30 and 0.43, respectively), consistent with previous studies

suggesting that C. perfringens spores might be a useful indi-

cator for Cryptosporidium (Ferguson et al., 1996; Payment and

Franco, 1993; Schijven et al., 2003). The settleable fraction of

fecal coliforms and E. coli are weakly correlated with settleable

concentrations of Giardia and Cryptosporidium, respectively

(Ra of 0.12 and 0.16, and p-values of 0.6 and 0.5, respectively),

results that are somewhat consistent with those that

demonstrated similarities in the settling velocities of these

organisms (Section 3.3). Of the organisms evaluated, bacterial

indicators (fecal coliform, E. coli, Enterococci) and C. perfringens

spores are probably the most reasonable way of attempting to

identify pathogen contamination when analyzing for the

pathogens themselves is impractical, however, dependence

on these indicators still leads to significant uncertainty.

Efforts were made to explore correlations between micro-

bial association with settleable particles and other water

quality parameters. In particular, a direct relationship

between microbial settleability (i.e. removal via centrifuga-

tion) and particle concentration might indicate differential

settling due to increased cell–particle collisions during

centrifugation. However, no significant relationships between

microbial settling behavior and particle concentration, TOC,

or temperature were identified. Additionally, although varia-

tions in pH can affect cellular attraction to particles, pH values

in the grab and intrastorm particles were relatively constant,

and were unlikely to have significantly contributed to

observed variations in settling behavior.

4. Conclusions

This study was initiated to explore several issues related to

the partitioning behavior of two pathogens, Cryptosporidium

and Giardia. While it is clear that the Kensico Reservoir has

Table 2 – Summary of (top) diameter and density of selected organisms gathered from literature, as well as measured and cal ulated (via Stokes Law) settling velocities forfree phase organisms, and (bottom) average settling velocity estimates for microbes in the settleable fraction

Fecal coliform E. coli Enterococci C. perfringens spores Fþ coliphage Som. coliphage Giar ia Cryptosporidium Particles

2–10 mm 10–20 mm

From literature

Diameter (mm) 1–4a 1–2.5b 0.87–1.01c 1–3d 0.025d 0.025d 10–1 e 4–6f

Density (g/cm3) 1.09–1.13g 1.23–1.38h 1.33–1.46j 1.33–1.46i 1.03 1.06k 1.52l 1.03l

Isoelectric point (pH) 2–4m 2–4m 2–4m 5.7n 4o 4o 2.2p 3.3p 1–2.5q

Aspect ratior 1–2.5 1 1–3 1–1.4 1 Variable

Setting velocities of free phase organisms (mm/s)

Experimentalj 1.4 0.35

Stokes Laws 0.14 0.14 0.14 0.5 0.0001 0.0001 1.25 0.62

Experimental values derived from this work

Average setting velocity of microbes in settable fraction (mm/s)

All sites (means) Dry 0.72� 0.80 0.37� 1.13 0.85� 0.73 1.18� 0.42 0.34� 0.77 0.68� 0.94 1.21� 0.3 0.73� 0.65 0.47� 0.25 0.80� 0.71

Wet 0.50� 0.52 1.21� 0.62 0.97� 0.65 0.92� 0.47 0.68� 0.94 0.0001 1.30� 0.2 1.09� 0.51 1.55� 0.48 2.07� 0.28

a Linsley et al. (1992).

b Holt (1994).

c Kokkinos et al. (1998).

d Lovins et al. (2002).

e Huang and White (2006).

f AWWA (1999).

g Bratbak and Dundas (1984).

h Tisa et al. (1982).

i Rohrmann and Krueger (1970).

j Medema et al. (1998).

k Metge et al. (2003).

l Calculated from settling velocities at the bottom of Table 2.

m Harden and Harris (1953).

n Minton and Clarke (1989).

o Lipp and Griffin (2004).

p Hsu and Huang (2002).

q Sharp et al. (2004).

r Calculated using the dimensions provided by the references in Table 2.

s Calculated using Stokes Law with the density and diameter of each microorganism from literature cited in top of Table 2.

wa

te

rr

es

ea

rc

h4

2(2

00

8)

44

21

–4

43

84

43

6

c

d

4j

2

4

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 4 2 1 – 4 4 3 8 4437

relatively low pathogen concentrations, these did generally

increase as the result of a storm event. Both Cryptosporidium

and Giardia exhibit evidence of a significant level of associa-

tion with settleable particles, with roughly 30% of these

organisms appearing in the settleable fraction during dry

weather periods, a value that increased somewhat during

storms. The partitioning behavior of these pathogens was

relatively similar to that of C. perfringens spores and the three

bacterial indicator organisms (fecal coliform, E. coli, Entero-

cocci), suggesting that these organisms may exhibit similar

transport behavior in the environment.

During wet weather events, pathogen concentrations

increased in all the tributaries examined. And, although the

cumulative loadings of Cryptosporidium and Giardia over the

course of a storm event are comparatively low relative to

indicator organisms, one storm can still result in a pathogen

loading equivalent to several month’s worth of dry weather

loading. This suggests that continuing efforts to reduce

pathogen loadings to the Kensico Reservoir (and perhaps

many other water bodies as well) should remain primarily

focused on the mitigation of stormwater-related non-point

sources.

Estimated settling velocities for the settleable fraction of

Cryptosporidium and Giardia were quite similar to one another

and significantly higher than experimentally derived values

for free phase organisms, particularly in the case of Crypto-

sporidium. Pathogen settling velocities were also similar to

those of the settleable particles, providing evidence that

pathogens identified as being in the ‘‘settleable fraction’’ are

indeed associated with particles in the water column. In

addition, settling velocities for C. perfringens spores, indicator

bacteria, and the pathogens were in the same range, sug-

gesting that the transport behavior may be reasonably similar.

Statistical tests revealed few strong correlations between

the concentrations or partitioning behavior of the indicator

organisms and pathogens. However, of the organisms

analyzed, bacterial indicators and C. perfringens spores were

the most useful indicators for the presence of Giardia and

Cryptosporidium. These results should provide a better under-

standing of the partitioning behavior of pathogens and indi-

cator organisms in a water column, information which can be

used to improve modeling of non-point microbial contami-

nants in the environment.

Acknowledgments

The authors wish to thank the New York City Department of

Environmental Protection (Contract No. 20070023132) for its

generous support of this work.

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