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Changes in Escherichia coli to Cryptosporidium ratiosfor various fecal pollution sources and drinkingwater intakes

Cindy Lalancette a,b,*, Isabelle Papineau a, Pierre Payment b, Sarah Dorner a,Pierre Servais c, Benoit Barbeau a, George D. Di Giovanni d, Michele Prevost a

aPolytechnique Montreal, Departement des Genies Civil, Geologique et des Mines, CP 6079, Succ. Centre-ville,

Montreal, Quebec, Canada H3C 3A7bCentre INRS-Institut Armand-Frappier, Institut National de Recherche Scientifique (INRS), 531 Boulevard des

Prairies, Laval, Quebec, Canada H7V 1B7c Ecologie des Systemes Aquatiques, Universite Libre de Bruxelles, Campus de la Plaine, CP 221, Boulevard du

Triomphe, B-1050 Bruxelles, BelgiumdUniversity of Texas-Houston School of Public Health, Center for Infectious Diseases, El Paso Regional Campus, 1101

N. Campbell CH 412, El Paso, TX 79902, United States

a r t i c l e i n f o

Article history:

Received 6 June 2013

Received in revised form

24 January 2014

Accepted 27 January 2014

Available online 5 February 2014

Keywords:

Cryptosporidium

Escherichia coli

Fecal indicators

Fecal pollution

Source water

Drinking water

* Corresponding author. Polytechnique Montrdes Mines, CP 6079, Succ. Centre-ville, Mont

E-mail address: cindy.lalancette@polymtl

http://dx.doi.org/10.1016/j.watres.2014.01.0500043-1354/ª 2014 Elsevier Ltd. All rights rese

a b s t r a c t

Assessing the presence of human pathogenic Cryptosporidium oocysts in surface water

remains a significant water treatment and public health challenge. Most drinking water

suppliers rely on fecal indicators, such as the well-established Escherichia coli (E. coli), to

avoid costly Cryptosporidium assays. However, the use of E. coli has significant limitations in

predicting the concentration, the removal and the transport of Cryptosporidium. This study

presents a meta-analysis of E. coli to Cryptosporidium concentration paired ratios to compare

their complex relationships in eight municipal wastewater sources, five agricultural fecal

pollution sources and at 13 drinking water intakes (DWI) to a risk threshold based on US

Environmental Protection Agency (USEPA) regulations. Ratios lower than the USEPA risk

threshold suggested higher concentrations of oocysts in relation to E. coli concentrations,

revealing an underestimed risk for Cryptosporidium based on E. coli measurements. In raw

sewage (RS), high ratios proved E. coli (or fecal coliforms) concentrations were a conser-

vative indicator of Cryptosporidium concentrations, which was also typically true for sec-

ondary treated wastewater (TWW). Removals of fecal indicator bacteria (FIB) and parasites

were quantified in WWTPs and their differences are put forward as a plausible explanation

of the sporadic ratio shift. Ratios measured from agricultural runoff surface water were

typically lower than the USEPA risk threshold and within the range of risk misinterpre-

tation. Indeed, heavy precipitation events in the agricultural watershed led to high oocyst

concentrations but not to E. coli or enterococci concentrations. More importantly, ratios

established in variously impacted DWI from 13 Canadian drinking water plants were found

to be related to dominant fecal pollution sources, namely municipal sewage. In most cases,

when DWIs were mainly influenced by municipal sewage, E. coli or fecal coliforms con-

centrations agreed with Cryptosporidium concentrations as estimated by the meta-analysis,

eal, Chaire Industrielle CRSNG en Eau Potable, Departement des Genies Civil, Geologique etreal, Quebec, Canada H3C 3A7. Tel.: þ1 514 340 4711; fax: þ1 514 340 5918..ca (C. Lalancette).

rved.

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1 151

but when DWIs were influenced by agricultural runoff or wildlife, there was a poor rela-

tionship. Average recovery values were available for 6 out of 22 Cryptosporidium concen-

tration data sets and concomitant analysis demonstrated no changes in trends, with and

without correction. Nevertheless, recovery assays performed along with every oocyst count

would have enhanced the precision of this work. Based on our findings, the use of annual

averages of E. coli concentrations as a surrogate for Cryptosporidium concentrations can

result in an inaccurate estimate of the Cryptosporidium risk for agriculture impacted

drinking water intakes or for intakes with more distant wastewater sources. Studies of

upstream fecal pollution sources are recommended for drinking water suppliers to

improve their interpretation of source water quality data.

ª 2014 Elsevier Ltd. All rights reserved

.

1. Introduction

Cryptosporidium is oneof themost frequently identified etiologic

agents associated with drinking waterborne illness in the

United States (US) (Craun et al., 2006) and in some European

countries such as England and Wales (Smith et al., 2006). In

reaction to the threats posed by this pathogen, increasing

rigorous regulations on its surveillance and removal were

promulgated in the US and in England (USEPA, 2005a,b; 1998,

Lake et al., 2007). Oocyst quantification through USEPA

Method 1623 (USEPA, 2005b) has so far been the reference

method but challenges remain, such as widespread afford-

ability (USEPA, 2005a). Consequently, most regulations rely on

widely recognized fecal indicators to assess themicrobiological

quality of surfacewater (Tallon et al., 2005; Ashbolt et al., 2001).

Several studies have shown positive correlations in different

types of aquatic systems between Cryptosporidium oocysts and

fecal indicators, turbidity and rainfall (Table S1). Significant

correlations have been reported for somatic coliphage (Fu et al.,

2010), Escherichia coli, fecal streptococci and total coliforms in

sewage (Reinoso et al., 2008) and for Clostridium perfringens

spores (Atherholt et al., 1998; Payment and Franco, 1993), E. coli

(McGuire et al., 2002; Wilkes et al., 2009) and rainfall events

(Kistemann et al., 2002; Schets et al., 2008) for surface water.

However, in most cases, studies conducted on surface water

and wastewater have shown that the concentrations of Cryp-

tosporidium oocysts were poorly or uncorrelated with those of

traditional indicators such as E. coli, total and fecal coliforms

and enterococci (Fu et al., 2010; Gibson III et al., 1998; Isaac-

Renton et al., 2005; Wohlsen et al., 2006; Payment et al., 2000;

World Health Organization (WHO) 2009, Payment and Locas

2010; Rose et al., 2004; Wu et al., 2011a) or with turbidity or

rainfall (Wilkes et al., 2009; Isaac-Renton et al., 2005). Reasons

for poor correlations may be inherent to limited data sets and

statistics using Cryptosporidium counts below detection limits

for linear correlations (Wu et al., 2011a). As a consequence,

costlyCryptosporidiummonitoring in sourcewater (USEPA, 2003)

and in treated drinking water (Drinking Water Inspectorate

(DWI) 2000) and increased treatment requirements for high

risk systems have been implemented as regulatory re-

quirements in the US and in England. However, for many other

countries, E. coli is still accepted as the best (and affordable)

surrogate of contamination by Cryptosporidium (WHO, 2006).

Relationships between E. coli and Cryptosporidium presence

and concentrations have been studied with the help of an

important survey conducted on American raw waters during

the regulatory development process (Information Collection

Rule: ICR and its Supplemental Survey: ICRSS). Results sup-

ported the decision not to require expensive Cryptosporidium

monitoring for small systems under the USEPA Long Term 2

Enhanced Surface Water Treatment Rule (LT2) if they had low

densities of E. coli (in lakes or reservoirs: <10; in flowing

streams: <50 CFU 100 ml�1) (USEPA, 2003). These levels were

considered too stringent for small systems andwere increased

up to 100 E. coli 100 ml�1 in the 2010 revisions (USEPA, 2010).

This E. coli level is considered to safely predict densities of

Cryptosporidium below 0.075 oocysts L�1 in source waters.

A better understanding of the implications of using in-

dicators to define pathogen concentrations could be obtained

by examining paired E. coli/Cryptosporidium ratios, further

referred to as Ec/Cr, in different types of pollution sources,

namely raw sewage (RS), treated wastewater (TWW) and

agricultural runoff (AgrRO). To help comparisons, a reference

ratio Ec/Cr of 1.3 � 104 can be calculated from these values.

The main objective of this study was to nuance the sig-

nificance of E. coli originating from various water sources as a

surrogate for Cryptosporidium. A meta-analysis was performed

using published and local data to calculate Ec/Cr ratios for the

primary sources of fecal contamination, namely raw sewage,

treated wastewater and manure contaminated agricultural

runoff. Ratios were shown to be very different for wastewater

effluents and agricultural runoff. The influence of various and

mixed fecal pollution sources on the ratio were investigated at

13 drinking water intakes (DWI). Monitoring recommenda-

tions are proposed for DWI where the ratio is insufficiently

conservative.

2. Material and methods

2.1. Description of the WWTPs surveyed

Two wastewater treatment plants (WWTPs) in the Greater

Montreal area (Canada) were investigated. Both received

municipal sewage from a combined sewer network with

minor industrial flows. Both included primary settlingwithout

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1152

coagulant addition, secondary biological treatment but no

disinfection. WWTP-S is equipped with aerated lagoons (AL)

offering an annual average hydraulic retention time of 16 day.

It receives wastewaters of a population of 63 000 inhabitants

with an average annual flow of 47.0 MLD. During our sampling

period (April to November 2009), the flow ranged from 12.1 to

73.1 MLD (average: 44.4 MLD). WWTP-V receives wastewaters

of about 30 000 inhabitants with an average annual flow of

20MLD and is equippedwith sequential bioreactors (SBR) with

an annual average hydraulic retention time of 7 h.

2.2. Description of runoff sample collection from theagricultural watershed

Microbial sampling was conducted from June to November

2009 in the Ruisseau au Castor watershed (11.2 km2) in

Southern Quebec (Canada). During this period, flow rates

(1 km upstream of our sampling site) ranged from 29 to

12 774 m3 d�1 with an average of 472 m3 d�1. Daily recorded

precipitation averaged 3 mm d�1 with a maximum of

54.2 mm d�1. The area drained by this small sub-watershed is

mainly agricultural (97%) and is predominantly used for dairy

and grain production. 52% of the cultivated land is tile drained

and inlets are also used for surface drainage (Michaud et al.,

2005). This sub-watershed contains 9 dairy farms, 1 pig farm

and 5 crop farms. Each dairy farm is composed of approxi-

mately 50 cows, 35 heifers, and 40 cow-heifers. Bovinemanure

from 7 of the 9 dairy farms is frequently land applied within

the subwatershed area, whereas hog manure is generally

spread outside of the study area. Most manure is spread in

spring and fall, but cattle slurry may be spread after each

forage harvest, depending on the climatic conditions. Runoff

from this subwatershed is considered to be representative of

agricultural fecal pollution sources influencing DWIs located

downstream of agricultural areas.

2.3. Description of data from other studies

Data for RW and secondary TWW were also gathered from

Rose et al. (2004) and agricultural runoff data were obtained

from NAESI (2006), Brookes et al. (2005) and Boyer and

Kuczynska (2003). For DWIs, data came from either Payment

et al. (2000) or from the DWI laboratories. Figures S2a and

S2b provide further details.

2.4. Microbiological and physico-chemical parameters

Sampling protocols included: (1) grab sampling of raw sewage

and treated wastewaters during the early morning peak hy-

draulic loading of urban wastewater, and (2) cartridge filtra-

tion for rural discharge water as described in detail in

Lalancette et al. (2012).

The Cryptosporidium Dual Direct Detection on Cell Culture

with Immunofluorescent Assay (3D-CC-IFA) protocol used in

this study for total oocyst counts (without consideration for

their infectious fraction) was previously described by

Lalancette et al. (2010), and environmental control experi-

ments and sampling protocols were reported in Lalancette

et al. (2012). Giardia cysts were quantified according to U.S.

EPAMethod 1623 (USEPA, 2005b) using an EasyStain GC combo

(BTF) and an Olympus BX51 microscope (Olympus, Tokyo,

Japan) equipped with FITC (U-N51006).

Fecal indicators were enumerated following standard

methods: enterococci (membrane filtration onmEI agar, Difco

No. 233320; USEPA Method 1600), total coliforms (membrane

filtration on MI agar, Difco No. 2124882; USEPA Method 1604)

and E. coli (membrane filtration on modified mTEC agar, Difco

No. 233410; USEPA Method 1603).

Standard methods were used for measuring total sus-

pended solid (TSS) concentrations (SM No. 2540 D) and

turbidity (SM No. 2130) using a Hach 2100AN.

2.5. Ratio and log removal estimates

Ratios were calculated using paired E. coli (or fecal coliforms

(FC) when E. coli concentrations were not available) and

Cryptosporidium positive counts from the same samples. The

impact of using Cryptosporidium recovery on the ratios was

investigated for 6 out of 22 data sets that included recovery

assays done on a limited subset (Table S2b). Corrected ratios

were computed based on these corrected concentrations.

When both indicators were available, E. coli concentrations

were used preferentially to FC concentrations. The ratio of FC

to E. coli typically varies from 0.6 to 0.99, depending of the

water type and themethods used (Garcia-Armisen et al., 2007;

Hamilton et al., 2005; USEPA, 2002a; Rasmussen and Ziegler,

2003). As this ratio was not available for all sources, FC con-

centrations were used without any correction. In a previous

study of two drinking water intakes in the study area, E. coli to

FC paired ratios were shown to have respective means and

standard deviations of 0.95 (CV ¼ 0.13) for one site and 0.85

(CV ¼ 0.24) for the other (n ¼ 68) (unpublished municipal

surveillance data).

Log removals of turbidity, fecal indicators and parasites

were calculated for above detection limit data collected

concurrently. Excel 2007 software (Microsoft, WA, USA) was

used to calculate p-values using two-tailed and unequal

variance Student’s t-test (unless otherwise stated, significance

was assessed at the p ¼ 0.05 level) and Pearson correlation

coefficients (r2p). For non-parametric analyses, Statistica 10

software (Statsoft, OK, USA) was used to calculate Spearman

rank order of correlation (rs), theManneWhitneyU test (MWU

test p-values) for comparing two groups and the Krus-

kaleWallis analysis of variance (KW ANOVA) for comparing

multiple groups.

3. Results and discussion

3.1. Ec/Cr ratio: an overview

Fig. 1 presents the ratios of E. coli (or FC) to Cryptosporidium

concentrations for the three water types investigated (RS,

TWW and AgrRO). Those values are also compared with the

ones measured at 13 drinking water treatment plant intakes

(DWI) impacted at various levels by contamination from

urban discharges and agricultural runoff. Their water sources

were a priori classified as being: 1- heavily impacted by

wastewater, 2- heavily impacted by agricultural runoff or 3- on

rivers with a very large capacity for dilution (1960e8560 m3/s)

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1 153

with few local sources of contamination. Information on the

discharges up flow of the DWIs is presented in Table S2a. For

example, in the case of Class 1 DWI-2 and DWI-4, primary and

secondary wastewater discharge represents from 15 to 48% of

the river flow and numerous combined sewer overflows

(CSOs) discharge regularly. DWI-1, also a Class 1, is located in a

dense urbanized area immediately downstream (<30 km) of

the wastewater discharges of 3.8 M inhabitants, including the

discharge from a large primary treatment facility with an

average dry-time flowof 2.5Mm3 d�1. On the other hand, DWI-

10 is located in a watershed with a land use of 46% forested,

46% farmland of which 65% is bovine and was classified as a

Class 2.

Also shown on this figure is the regulation threshold of 100

FIB 100ml�1 to 0.075 oocyst L�1 (ratio of 1.3� 104) of the USEPA

revised small systems regulation (USEPA, 2010) that triggers

required Cryptosporidium monitoring. When the observed ra-

tios exceeded the USEPA regulation threshold ratio, E. coliwas

considered as conservative indicator of Cryptosporidium. A

higher ratio corresponded to higher concentrations of FIB

relative to lower oocyst concentrations. Conversely, a lower

ratio was considered to present a risk of misinterpretation

because relatively lower concentrations of FIB were measured

although higher concentrations of oocysts were present.

Fig. 1 e Ratios of Ec/Cr from raw sewage (RS), treated

wastewater (TWW), agricultural runoff (AgrRO) and a

selection of Canadian drinking water intakes (DWI) with

their water source classification (1- Heavily impacted by

WW; 2- Heavily impacted by AgrRO; 3- With large capacity

for dilution and few local sources of contamination). Dotted

line represents a ratio threshold calculated from USEPA

Long Term 2 Enhanced Surface Water Treatment Rule (LT2

USEPA, 2010) small system regulations. In addition to the

data collected in the present study, data from Rose et al.

(2004), Brookes et al. (2005), Boyer and Kuczynska (2003)

and NAESI (2006) were used. Fecal coliforms were used

instead of E. coli for: Rose RS and TWW; DWI-1, -2, -3, -5, -7,

-8, -10, -11, -12, -13 and Boyer AgrRO data. One extreme

Rose RS point removed: 3 3 107 fecal coliforms 100 mlL1,

0.24 Cryptosporidium LL1, for a ratio of 1 3 109. OM: OldMan

Riv., SN: South Nation Riv. Boyer raw data were not

available and seasonal medians were used. * This study.

A lower ratio for a source water could translate into the defi-

nition of lower microbial removal goals and may result in the

provision of insufficient treatment barriers. Fig. 1 shows high

Ec (or FC)/Cr ratios for municipal wastewaters, low ratios for

agricultural runoff and a mixture of ratios coming from vari-

ously impacted DWI. Detailed analysis of Fig. 1 are provided in

upcoming water type sections integrating raw data from Fig. 2

and comparative statistics using KruskaleWallis analysis of

variance (KW ANOVA) in Table S2c.

Fig. 2a presents the overall locational relationship between

the standard criterion of average concentrations of E. coli (or

FC) and Cryptosporidium for all data from RS, TWW, DWI and

AgrRO (rs ¼ 0.85, n ¼ 22, p-value <0.05). In this case, the grey

zone identifies the regulatory level used by USEPA to require

Cryptosporidium monitoring for small drinking water systems,

while the black dash line represents an extension of this

reference ratio. When segregating data, significant correla-

tions were also observed for all DWI averages (rs ¼ 0.81, n ¼ 13,

p-value <0.05). The areas under the reference ratio lines rep-

resented the zones where risks of misinterpretation may lead

to an underestimation of Cryptosporidium risk, as they corre-

sponded to conditions of elevated oocyst concentrations

while indicator concentrations were low. Furthermore, the

grey zones represented situations for which no Cryptospo-

ridium monitoring would be required based on low FIB con-

centrations. Three locations, DWI-9, -13 and OM AgrRO fell

into this zone. When considering the average values of Ec/Cr

ratios (Fig. 1) as opposed to locational standard E. coli and

Cryptosporidium averages (Fig. 2a), nine other locations/sour-

ces fell under the reference ratio (DWI-6 to�13 and Boyer, OM,

this study AgrRO and this study TWW), showing the impor-

tance of the computational approach to estimate fecal indi-

cator relationships to Cryptosporidium. These results show that

very high concentrations of FIB (>103 CFU 100 ml�1) were

clearly associated with elevated oocyst concentrations. In the

case of FIB values between 102 and 103 CFU 100ml�1, locations

most impacted by agricultural sources may be misclassified.

3.2. Ec/Cr ratios in raw sewage and treated wastewater

Ratios for raw sewage were calculated from data from two

collection systems in Canada (Lalancette et al., 2012) and from

6 collection systems in the US (Rose et al., 2004). Detailed in-

formation on theWWTPs is provided in Lalancette et al. (2012)

and Rose et al. (2004). The mean ratios measured in the Ca-

nadian plants were 5.4 � 106 (CV ¼ 0.81) when not corrected

for recovery and 9.5 � 105 (CV ¼ 0.81) when corrected. They

were similar to previous reports in Canada and Spain which

ranged from 4.9 � 105 to 6.0 � 106 (Reinoso et al., 2008;

Payment et al., 2001). The mean ratios reported for the com-

bined data from the 6 southern US plants were higher (but not

significantly) at 8.2 � 106 (CV ¼ 2.3) for FC/Cr. Detailed raw

sewage data (n ¼ 29) are presented in Fig. 2b. Clearly no cor-

relation was observed, but paired data were found conserva-

tively above the reference ratio line. As also shown in Fig. 1, an

underestimation of the risk was unlikely in the case of recent

raw sewage discharges (treatment plant by-pass, combined

sewer overflow, cross connections, unconnected/mis-

connected collectors), since the RS ratios remained consis-

tently higher than the reference ratio. Using non-parametric

Fig. 2 e Relationship between E. coli or fecal coliforms (Ec or FC) and Cryptosporidium for paired positive samples in a) all raw

sewage (RS), treated wastewater (TWW), agricultural runoff (AgrRO) and drinking water intakes (DWI) averages; b) all RS raw

data; c) all TWW raw data; d) all AgrRO raw data and e) all DWI raw data employed in ratio calculations. Gray dashed lines

represent the USEPA LT2 2010 small systems regulation and the gray areas indicate the zone of riskmisinterpretation. Black

dashed lines show ratio regulation threshold of 1.3 3 104 CFU Ec or FC/Cr oocysts. Sources of data are detailed in Table S2a

and b.

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1154

analysis of variance (KW ANOVA), RS ratios (n ¼ 29) were not

significantly distinct from ratios of TWW (n ¼ 30; p-value

>0.05) but significant differences (p-values <0.001) were noted

for ratios from AgrRO (n ¼ 57) and ratios from only one DWI of

Class 1 (-5) and all DWI of Class 2 (-7, -10, -12) and Class 3 (-3,

-6, -8, -9, -11, -13) (Table S2c).

Fig. 1 shows that TWW ratios from the Rose TWW (n ¼ 22)

and from this study TWW (n ¼ 8) varied significantly (MW U

test p-value <0.01) ranging from 5.0 � 104 (CV ¼ 1.96) (this

study) when not corrected and 1.3 � 104 (CV ¼ 1.96) when

corrected for recovery, to 1.3 � 106 (CV ¼ 1.62) (Rose et al.,

2004). The ratios measured in the current study fall below

the risk reference ratio line indicating relatively low E. coli

concentrations in the presence of higher concentrations of

oocysts while data from Rose et al. (2004) in secondary treated

effluent remain well above the reference level. These

variations are further illustrated when examining all sec-

ondary TWW raw data (n ¼ 30) in Fig. 2c where D-TWW shows

1 out of 4 and S-TWW 2 out of 3 raw paired data in the zone of

risk misinterpretation. For S-TWW, longer retention times for

solids resulted in higher log removals of FIB as compared to

parasites (Fig. 3). These variations show the large impact of

specific treatment on the differential removal of indicators

and parasites by biological wastewater plants.

Differences in these ratios reflect the various conditions of

treatment that will determine the relative removal of FIB vs.

oocysts. This is demonstrated in Fig. 3 which summarizes the

performance of various WW treatment processes. Clear

trends are observed with clustered removal levels of particu-

late related parameters (turbidity and TSS), FIB and parasites.

Log removals of FIB achieved by the WWTP-S lagoons oper-

ating at an average hydraulic retention time of 16 days were

Fig. 3 e Log removal of total suspended solid (TSS) and

turbidity, FIB (enterococci, total coliforms and E. coli) and

pathogenic protozoa (Cryptosporidium and Giardia) by an

aerated lagoon WWTP-S (n [ 4e13), a sequential

bioreactor WWTP-V (n [ 2e8) and combined data from 6

secondary treatment WWTPs reported by Rose et al. (2004)

(n [ 31e33). Paired data were used to calculate log

removals and all values below detection limits (BDL) were

removed, including the extreme data removed from Rose

RS in Fig. 1.

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1 155

elevated and medians ranged from 4.4 to 4.6, while they were

only 1.9e2.3 for the sequential bioreactor treatment atWWTP-

V. These values are in agreement with those reported by Rose

et al. (2004) and by several other studies showing approxi-

mately 2-log removals by activated sludge secondary biolog-

ical treatment. When combining the log removal data from

our two WWTPs, strong correlations (n ¼ 14, 15; r2p � 0.9; p-

values < 0.05) were found amongst FIB removals (E. coli, total

coliforms, enterococci). Modest removals of Cryptosporidium

(<1.5 log) were measured in both plants in agreement with

previous reports varying from 0.7e2 (Crockett, 2007), 0e2.2

(Rose et al., 2004), 0.7 (Cheng et al., 2012), 0.02e1 and 0.6

(Medema and Schijven, 2001).

In general, protozoa were removed less efficiently than

FIBs and mechanisms responsible for the inactivation and/or

the destruction of protozoa by biological treatment processes

remain to be detailed (Fayer and Xiao, 2007). Also, Cryptospo-

ridium varies with infection rates within the population (Fayer

and Xiao, 2007). Moreover, Fig. 2c shows treatment having

variable impact on daily concentrations of FIB and Cryptospo-

ridium, thereby making their relationship less predictable.

These are possible explanations for the variable Ec(FC)/Cr ra-

tios following wastewater treatment depicted in Fig. 1.

3.3. Ec/Cr ratios from agricultural runoff

At the Ruisseau au Castor discharge point, monitoring of E. coli

and oocysts in agricultural runoff during this study revealed

ratios lower than threshold of 2.6 � 103 (CV ¼ 0.88) (corrected

for recovery: 7.3 � 102, CV ¼ 0.92). Fig. 1 also presents ratios

calculated from a wide Canadian study assessing water

pollutants in agricultural area (NAESI, 2006) with values of

mean ratios of 3.9 � 104 (CV ¼ 1.54) in the South Nation River

(NAESI-SN) and 4.6 � 103 (CV ¼ 1.83) in the Oldman River

(NAESI-OM), while ratios inferred from Boyer and Kuczynska

(2003) show seasonal medians of 4.2 � 102 (CV ¼ 1.38) and

ratio of 2.0 � 104 (CV ¼ 0.60) can be derived from Brookes et al.

(2005) data. Fig. 2a clearly shows that locational average ratios

in sites with dominant agricultural discharges fall near or

below the USEPA reference ratio. When considering detailed

data for all sites (n ¼ 57; Fig. 2d), most samples remain under

the reference ratio except for those from the NAESI-SN. These

samples originate from a river (2.5 Mm3 d�1) located in an area

of mixed but predominantly dairy farming with potential WW

discharges from several small municipalities (NAESI, 2006).

Detailed AgrRO raw data presented in Fig. 2d also show that

the NAESI-SN data are less clustered ranging from 9 to 2000 E.

coli 100 ml�1 and from 0.03 to 9 oocysts L�1. Comparative

statistics (KW ANOVA) demonstrated significant differences

for AgrRO ratios (n ¼ 57) when compared to ratios from RS and

TWW (n ¼ 29, 30; p-values < 0.001) but no significant differ-

ences are noted for ratios from all DWI (p-values> 0.05) (Table

S2c).

Temporal variations of concentrations as a function of

rainfall and river flows in the Ruisseau du Castor are pre-

sented in Fig. 4. A threshold precipitation value of

1.1 mm d�1 was required to observe a response from the

watershed (Michaud et al., 2005) and precipitation exceeded

this value on 50% of sampling days. Flow rates during the

sampling days varied 24-fold and corresponded to typical

values during the period of May to November during which

land application of manure is allowed. The maximum pre-

cipitation over a 24 h period (54 mm d�1) was observed at the

end of July (day 210; flow rate of 125 m3 d�1) coinciding with

the highest Cryptosporidium oocyst concentration

(15 oocysts L�1). Interestingly, elevated concentrations of

Giardia cysts, E. coli, enterococci were not observed on that

day, evidence of the intrinsic variability of Cryptosporidium

oocysts in environmental samples. Parasite concentrations

were not detected (below the detection limits (BDL) of

0.02e0.24 (oo)cysts L�1) for some sampling dates during the

late summer and fall periods: on days 266, 280 and 309 for

Giardia, and on day 266 for Cryptosporidium. Different sedi-

mentation and/or transportation behaviors were observed

for Cryptosporidium oocysts as compared to E. coli in vege-

tative treatment areas (Berry et al., 2007). Enterococci are shed

in lower numbers as compared to E. coli (Cabral, 2010) and

they may become undetectable earlier during an event.

River concentrations of enterococci were associated with

the first flush of runoff events, while in contrast, elevated E.

coli river concentrations persisted during the whole event

suggesting an unlimited supply (Cinque and Jayasuriya,

2010).

In summary, high oocysts concentrations in the presence

of low tomoderate E. coli concentrations produce low ratios in

agricultural runoff, a situation which could be wrongly inter-

preted as a low Cryptosporidium risk. The fate of protozoa and

FIBs vary widely when land application serves as a barrier

between manure and source water (Wilkes et al., 2009; King

and Monis, 2007; Dorner et al., 2007). Ratios from this study

suggest that the USEPA reference ratio is not adequate to

Fig. 4 e Concentrations of Cryptosporidium oocysts, Giardia cysts, E. coli and enterococci with daily precipitation and rural

Ruisseau au Castor discharge daily stream flow rate for year 2009. ((Oo)cyst data were corrected for recoveries measured for

each sample).

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1156

predict Cryptosporidium risk in agricultural runoff and rivers

impacted by agricultural runoff.

3.4. Ec/Cr ratios at drinking water intakes (DWIs)

Three classes of DWI were initially defined with regards to

their dominant fecal pollution sources (Table S2a). Class 1

included water sources heavily impacted by municipal

wastewater, Class 2 water sources were heavily impacted by

agricultural runoff and Class 3, water sources had a large ca-

pacity for dilution and few local sources of contamination.

Since FIB/Cr ratios primarily serve to determine water quality

types to set regulatory monitoring and treatment re-

quirements for this protozoan, the variations of this ratiowere

investigated in 13 DWI paired data ratios placed in decreasing

ratio order with their corresponding dominant fecal pollution

sources classification (Fig. 1). Detailed information on the

relative contribution of fecal sources and their classification is

presented in Table S2a. DWI ratios can be considered con-

servative for protozoan risk when ratios are higher than the

USEPA reference threshold.

DWIs can be sorted according to their ratio value and

interpreted in light of the dominant sources of fecal contam-

ination classification identified in Table S2a. The first group of

DWI ratios (1e4) are above the threshold with DWI-1 and -2

being highly variable, similar to ratios observed in raw

sewage. The DWIs that show a ratio value above the reference

threshold are not statistically distinct from RS and TWW

sources (KW ANOVA p-value >0.05) with the exception of

DWI-3 (Class 3) when compared to RS (KW ANOVA p-value

<0.001) (Table S2c). DWI-1 is located downstream of a very

large metropolitan area (pop. 3.8 M) and wastewater plumes,

RS, CSOs, sanitary sewer overflows can influence the water

intake (Class 1) (Dorner et al., 2012). Most upstream waste-

water plants did not disinfect their effluents at the time of

sampling and the largest WWTP (dry time flow of

2.5 � 106 m3 d�1) uses physicochemical treatments with

known important performance fluctuations (Gouvernement

du Quebec, 2008). DWI-2 is located on a small urban river of

w30 km receiving TWW, sporadic sewage by-pass, sanitary

and combined sewer overflow (SSO and CSO) discharges

from a population of 250k residents (Class 1) (Payment, 2003).

DWI-3 intake was initially categorized as a Class 3 water

source based on its origin. DWI-3’s water is supplied by the St-

Lawrence River that has a large capacity of dilution. However,

it then transits via an urban canal located in a dense urban

area and local sources of fecal contamination entering the

canal are suspected. Studies are presently being performed to

identify municipal fecal sources in proximity (unpublished

data) and to quantify known wildlife sources of fecal inputs.

DWI-4 is located upstream of DWI-2 and receives discharges

from a nearby population of 125k residents (Class 1). The

observed variability of DWI-1 and -2 on the E. coli to Crypto-

sporidium ratios in rivers with dominant wastewater fecal

sources may have several causes. First, from an epidemio-

logical standpoint, E. coli is a natural inhabitant of warm-

blooded animals but the prevalence of Cryptosporidium fol-

lows disease cycles (Percival et al., 2004). Secondly, waste-

water treatment may be selective and removal efficacies vary

widely by treatment type and with season, especially for

Cryptosporidium, as shown in Fig. 2. The province of Quebec

(Canada) instituted a moratorium on chlorination of waste-

water which may positively favor the presence of microbial

indicators in TWW. The relative abundance of pathogens vs.

indicators in raw and treated WW will impact the risk evalu-

ation as shown by Soller et al. (2010a). For a given concentra-

tion of FIB in recreational water, raw sewage represents a

source of contamination with a lower risk of pathogens than

TWW. When evaluating Cryptosporidium risk on the basis of

FIB concentration, one could argue that treated wastewater

discharges represent a situation for more frequent misclas-

sification of protozoan risk. However, Fig. 2e plotting raw data

show that clusters do remain in the conservative zone for

DWI-1 and mostly for DWI-2 (1/5), -3 (5/47) and -4 (3/12).

The second group comprising DWI-5 to -8 have ratios near

the threshold value. Most of DWI of this group show signifi-

cant differences (KW ANOVA p-value DWI-5, -7, -8 <0.1; DWI-

6 ¼ 0.4) with RS but not with TWW and AgrRO ratios (KW

ANOVA p-value > 0.05) (Table S2c). DWI-5 is variable and

located on a heavily urbanized river with moderate flow

(1094 m3 s�1) known to be impacted by CSOs from the sur-

rounding densely urbanized area (2007e2009 averages of 731

CSO events per year)(Class 1). DWI-6 is located on a large river

with modest upstream fecal pollution sources which are

originating from a nearby village (pop. 8000) andmany distant

small communities, wildlife from the forestry dominated area

and from nearby bird colonies (Class 3). DWI-7 is variable and

draws water from a low flow (192 m3 s�1) agricultural river

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1 157

which also receives discharges from a population of approxi-

mately 165k residents, including one large city with a popu-

lation of 157k located at about 90 km upstream in the

watershed (Class 2). DWI-8 is located on the same river as

DWI-1 but on the opposite shore receiving mainly water from

main flow of the St-Lawrence River (Class 3) (Comite Zone

d’Intervention Prioritaire (ZIP) Seigneuries 2003).

Finally, the group showing the lowest ratios (DWI-9 to �13)

have averages below threshold and are the least variable. All

DWI of this group show significant differences with RS, most

of DWI (-9, -11, -13) with TWW (KW ANOVA p-values < 0.05)

and no difference with AgrRO (Table S2c). DWI-9 is located on

a large river with modest upstream fecal pollution sources

which are originating from a village (pop. 8000) and many

distant small communities, urban stormwater runoff, and

also wildlife from the forestry dominated area (Class 3). DWI-

10 and DWI-12 differ but are dominated by agricultural use

(Class 2), with modest low density upstream populations of

50k habitants (Table S2a). DWI-11 and DWI-13 draw water

from the St-Lawrence River with a large capacity for dilution

with an upstream Great Lakes population of >33 million res-

idents but there are no wastewater discharges immediately

upstream (Class 3).

In fact, DWI-1, -2, -4, -5, -7, -8, -10, -12 averages fell above

USEPA LT2 small systems Bin 1 classification (100 E. coli

100 ml�1) while DWI-1, -2, -4, -5, -7, -8, -9, -10, -12, -13 were

above Bin 1 at 0.075 oocyst L�1 (Table S2b); average data

demonstrated for all but DWI-9 and -13 Cryptosporidium pres-

ence being conservatively explained by E. coli concentrations

(Fig. 2a) but none of them had conservative paired data ratios

all the time (Fig. 1).

3.5. General discussion

Average concentrations of E. coli and Cryptosporidium decrease

along with ratio values, with highest values found in raw

sewage and lowest in river intakes located downstream of

forested watershed areas (Table S2b). Overall, log transformed

E. coli and Cryptosporidium averages are strongly correlated

(Fig. 2a; rs ¼ 0.85, n¼ 22, p-value<0.05). However, variations in

ratios clearly show that reductions are greater for E. coli,

raising the issue of establishing one reference ratio for the

determination of monitoring requirements that would be

valid for all sources of fecal contamination. Indeed, in Table

S2c, KW ANOVA showed significant differences (p-values

<0.001) in ratios from RS (n ¼ 29) and TWW (n ¼ 30) and ratios

from AgrRO (n ¼ 57). Plausible explanations for these obser-

vations include: (1) the initial ratio at the source of production,

(2) the impact of environmental factors, such as travel time

and initial treatment; and (3) the presence of methodological

bias.

The source of fecal contamination and the associationwith

organic matter and particles are critical factors to consider.

Recreational water risk models demonstrate lower risk of

gastrointestinal illness when fecal contamination was origi-

nating from gull, chicken or pig feces compared to cattle feces

and human sources (Soller et al., 2010a). The type of fecal

contamination also influences aggregation and settling, two

importantmechanisms to predictmicrobial transport and fate

in receiving lakes and rivers (Brookes et al., 2005; Cizek et al.,

2008; Medema et al., 1998; Passerat et al., 2011; Krometis

et al., 2010). Microorganisms from raw sewage and TWWmay

remain incorporated in fecal particles or associated with

organic matter or treatment flocs. On the other hand, micro-

organisms leaching from fecal pats to subsurface or surface

runoff may be transported more easily through soil particles

than aggregated microbes. Brookes et al. (2005) suggested that

oocysts may show higher affinity for organic matter (e.g.

sewage effluent) than inorganic particles (e.g. soil) and may

tend to be transported as small particles, whereas bacteria are

associatedwith the transportof largerparticles.The ratioof FIB

to Cryptosporidium is influenced by selective removal of FIBs by

WWtreatment andagricultural runoff. The typeofwastewater

treatment will affect bacteria and protozoan die-off/removal

rates (Reinoso et al., 2008; Rose et al., 2004; Lalancette et al.,

2012; Cheng et al., 2012). WW disinfection by chlorination will

depress FIBs (Soller et al., 2010b) but not oocysts (Rose et al.,

2004). Die-off following manure application, with or without

previous treatment, is determined by several factors including

temperature, ammonia, desiccation, solar inactivation and

predation (Lalancette et al., 2012; King and Monis, 2007;

Medema et al., 1997; Jenkins et al., 2011; Oliver et al., 2006;

Karim et al., 2004). Wildlife, such as birds and waterfowl, may

also contribute to bacteria and protozoa presence in source

water by directly depositing their fecal matter in water. In the

present study, DWI-11 and -3 and DWI-9 and -6 source waters

(all Class 3) could be grouped together as they use almost the

same water. Interestingly, DWI-9 is upstream of DWI-6 while

DWI-11 is upstream of DWI-3. Large bird colonies are located

between both groups of DWIs. Ratios for DWIs located up-

stream of bird colonies are in both cases higher than ratios

located downstream. Althoughwaterfowl and Canadian geese

are known to be sources of Cryptosporidium oocysts (Graczyk

et al., 1998; Zhou et al., 2004), these higher ratios downstream

of the bird colonies demonstrated a relatively higher input for

FIB than oocysts. Also, seasonal variation and its interrelated

variations of flow rate, precipitation, and cycles of disease,

solar UV and temperaturemay affect survival and fate of E. coli

and Cryptosporidium in variousways depending on their origin.

Environmental microbial behaviors are highly complex and

often put into a black box (USEPA, 2010; Soller et al., 2010a) and

are of lesser importance the closer thepoint ofmeasurement is

to the fecal source in space and in time.

For the group of DWIs categorized in the zone of risk

misinterpretation, it should be noted that relatively higher

Cryptosporidium concentrations may reflect methodological

discrepancy between E. coli culture methods and microscopic

detection of oocysts. Indeed, for DWI-13 and DWI-11, fecal

contamination originates from the Great Lakes, with the first

major city 300 km upstream as shown by the persistence of

recalcitrant pharmaceuticals (Daneshvar et al., 2012). It is

plausible that environmentally aged oocysts, were still

detectable via microscopy while some E. coli could not be

detected by culture-based methods. Methods assessing oo-

cysts infectivity could help better evaluate Cryptosporidium

risk (Lalancette et al., 2010).

A significant source of methodological bias could be the

treatment of non-detect data. In this study, only paired positive

samples were considered as the research weighed the value of

E. coli as an indicator of the presence and the concentration of

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1158

Cryptosporidium oocysts. Considering the BDL data is critical to

establish occurrence especially when estimating risks using

QMRA models (Petterson et al., 2006). However, a study

reviewing 40 years of indicator and pathogen data collection

and correlationshighlighted thenumber of samplespositive for

pathogens as being one of the most important factors in

determining correlations (Wu et al., 2011b). Furthermore, the

purpose of our evaluation of the Ec/Cr ratio is to verify its ability

to determine safely the potential of Cryptosporidium oocysts

contamination. By using only data greater than the DL for

Cryptosporidium oocysts, this verification addresses the poten-

tial situations most at risk of misclassification.

Methods for detecting environmental oocysts often show

great variability due to challenging sample matrices, low

concentration of oocysts, and the skill level of the practitioner

(Petterson et al., 2006). In order to improve estimates, labora-

tories rely on recovery assays with spiking. However, recovery

assays are not always performed as they involve significant

time, effort and cost (Ongerth, 2013; Dechesne et al., 2006) and

uncorrected values are used for regulatory purposes. When

recoveries are assessed, little consensus on the method

employed leads to various protocols (e.g. frequency of spiking,

spiking techniques, transformations of monitoring data)

(Ongerth, 2013; Petterson et al., 2007). To enhance accuracy,

some matrix spikes are required for the USEPA protocol for

Cryptosporidium with a minimum frequency of 1 matrix spike

sample per 20 field samples for each individual source (USEPA,

2012). More recently, an argument has been presented that the

systematic measurement of recovery rates should be required

for Cryptosporidium monitoring (Ongerth, 2013; USEPA, 2012).

Althoughmost recent research focuses on parasite recoveries,

similar concerns have beenbe raisedon the impact ofmatrices

on the recoveryof fecal coliformsand E. coli (Olstadt et al., 2007;

USEPA, 2002b). Targeting total coliforms, Olstadt et al. (2007)

showed significant variability in recovery between these rela-

tively similar water types within a single protocol for nine

methods. The value of using raw ratios without correction for

recoveries, as isprescribed inmost regulations, is subject to the

same limitations as the interpretation of true Cryptosporidium

occurrence. However, using ratios computed from paired

measurements subjected to the same matrix effect may pro-

vide a better trending tool as bothmeasurements are subject to

the same temporal interferences.

Average Cryptosporidium recoveries were available for some

of the data sets (6/22) used for this analysis (Table S2b). Cor-

rected ratios calculated for those data sets show that account-

ing for recoverydoesnot change the trendsof ratios in reference

to the dominant source of fecal contamination sources at DWIs

(Fig. 1). Using a global average recovery value such as the 40%

(CV¼ 56%) from the USEPA LT2ESWTR (n¼ 3370) would simply

cause a shift of the ratios and introduce more uncertainties, as

the variations in recovery can be considerable (10e90%) at each

sample site over a year (USEPA, 2005a; Ongerth, 2013; Petterson

et al., 2007; Schmidt and Emelko, 2011).

This study highlights the importance of the character-

ization of fecal pollution sources influencing DWIs not as

averages but considering paired raw data sets. For those

influenced by nearby municipal wastewater outfalls, routine

E. coli or fecal coliforms measurements appear to be conser-

vative indicators of the potential presence of Cryptosporidium

oocysts. Nevertheless, when fecal pollution originates from

distant municipal, rural or forested areas, Cryptosporidium

monitoring may be prudent, at least, the introduction of a

more environmental resistant fecal indicator such as C. per-

fringens should be considered (Dorner et al., 2007). However,

more data are needed to understand the human health risk

posed by Cryptosporidium spp. originating from distant

wastewater, rural and forested sources since they may

represent non-human pathogenic Cryptosporidium species

(Ruecker et al., 2007) or be non-infectious (Lalancette et al.,

2012).

4. Conclusions

Fecal indicator bacteria (FIB), such as fecal coliforms and E.

coli, are commonly employed to assess the potential presence

of pathogens in drinking water treatment plant intakes

(DWIs). However, their relationships to Cryptosporidium con-

centrations have never been straightforward and detection of

E. coli or fecal coliforms is still considered a good economical

alternative to costly Cryptosporidium monitoring. Concentra-

tion ratios of E. coli to Cryptosporidium originating from distinct

fecal pollution sources and those found at DWIs were

compared to USEPA LT2 regulations for small systems (USEPA,

2010). Overall, this study shows that when considering ratios,

FIB concentrations did not always predict high concentrations

of Cryptosporidium oocysts, although averages often did,

masking Cryptosporidium risk. From the perspective of raw

sewage, E. coli (or fecal coliforms) concentrations proved to be

a conservative indicator of Cryptosporidium concentrations

when using both ratio and detailed raw data. For secondary

treated wastewaters, ratios typically remained in the conser-

vative zone but sporadic exceptions occurred, likely due to

fluctuations in treatment efficiency. Longer retention time

during biological treatment resulted in a higher removal of FIB

but not Cryptosporidium oocysts. Ratios from agricultural

runoff were typically below the USEPA risk threshold, unless

municipalities were also present upstream. Peak concentra-

tion of oocysts in runoff was related to peak precipitation,

which was not the case for FIB. DWIs with ratios in the con-

servative zone were mainly influenced by nearby municipal

fecal pollution sources and results support FIB as good in-

dicators of Cryptosporidium. When themeasurable influence of

municipal fecal pollution sources decreased, ratios decreased

into the zone where Cryptosporidium concentrations were not

explained by FIB concentrations.When available in this study,

concomitant analysis of ratios including Cryptosporidium

average recoveries were performed and did not influence the

study conclusions. However, accounting for variations in re-

coveries and measurements of microorganisms in highly

heterogeneous environmental matrixes remains a desirable

approach to better understand their behavior in the environ-

ment. Finally, these results strongly suggest that E. coli or fecal

coliforms are potentially good indicators of Cryptosporidium

concentrations when source waters are impacted by recent

and nearby municipal sewage, but not for sources dominated

by agricultural or rural fecal pollution sources or more distant

wastewater sources. Addressing the trade-off between time

and effort to produce recovery corrected data and the

wat e r r e s e a r c h 5 5 ( 2 0 1 4 ) 1 5 0e1 6 1 159

production of more accurate concentrations remains a chal-

lenge for the water industry.

Acknowledgment

This study was supported by the NSERC Industrial Chair on

Drinking Water at the Polytechnique Montreal, which is

jointly funded by the City of Montreal, John-Meunier/Veolia

Water, the City of Laval, and the Natural Sciences and Engi-

neering Research Council of Canada. We also thank Erin

Gorman and Ian Douglas from City of Ottawa.

Appendix A. Supplementary data

Supplementary data related to this article can be found at

http://dx.doi.org/10.1016/j.watres.2014.01.050.

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