The Pennsylvania State University
The Graduate School
College of Agricultural Sciences
MICROBIAL SURVEY OF PENNSYLVANIA SURFACE WATER USED FOR
SPECIALTY CROP IRRIGATION AND DEVELOPMENT OF SAMPLING,
HANDLING, AND SHIPPING PROCEDURES FOR SURFACE WATER TESTING
A Thesis in
Food Science
by
Audrey H. Draper
© Audrey H. Draper
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
December 2012
ii
The thesis of Audrey H. Draper was reviewed and approved* by the following:
Luke F. LaBorde
Associate Professor of Food Science
Thesis Co-Advisor
Stephanie Doores
Associate Professor of Food Science
Thesis Co-Advisor
Hassan Gourama
Associate Professor of Food Science
Robert F. Roberts
Professor of Food Science
Interim Head of the Department of Food Science
* Signatures are on file in the graduate school
iii
ABSTRACT
Surface water is widely used for irrigating fruit and vegetable crops in the United States.
Compared to municipal and groundwater sources, its microbial quality is highly variable because
it is open to sudden and unexpected environmental contamination. Recent produce-associated
foodborne illness outbreaks have been attributed to contaminated irrigation water. In agricultural
food production, there are no nationally enforced microbial standards for surface water used for
crop irrigation. Standard methods for microbial water testing mandate that surface water be
analyzed within six hours of sample collection. This study will examine microbial levels in
Pennsylvania surface waters, the relationship between microbial indicator organisms and the
presence of human pathogens, and the effect of sample temperature and time on changes in
microbial levels. Methods to develop a mail-in surface water testing kit will be explored.
Water samples from surface water sources used for irrigation in Southeastern
Pennsylvania were collected from 39 farms over a two year period. Samples were analyzed for
six microbial indicator organisms (total plate count, Enterobacteriaceae, coliform, fecal
coliform, E. coli, and enterococci), two human pathogens (Salmonella spp. and E. coli
O157:H7), and 11 physical and environmental characteristics (turbidity, conductivity, pH,
dissolved oxygen, air and water temperature, sampling day and accumulated precipitation levels,
algae growth, water movement, and sunlight). Samples showed high variability both between
and within sampling sites. Human pathogens were not recovered in the first year of sampling
using standard methods. Therefore, new methods were evaluated for use during the second year
of sampling which included recovery and selective steps. During the second year of the survey,
Salmonella was recovered from 5 water samples; however, confirmed E. coli O157:H7 isolates
were not recovered from any samples. Current standards based on microbial indicator organisms
iv
were not shown to be strong predictors of water safety. Water pH was the only physical or
environmental characteristic which demonstrated significant correlation with levels for each
indicator organism.
Water samples were mailed overnight from Reading, PA to State College, PA in order to
determine maximum temperatures reached during mailing. All samples reached an approximate
equilibrium with the warmest temperatures experienced during mailing, which was 39°C. The
effects of mailing time and temperature on indicator populations was then studied by holding
surface water samples at 4, 10, 21, 30, and 39°C. Total plate count, coliform, fecal coliform, E.
coli, and enterococci levels were determined within one hour of sample collection, and 6, 18, and
30 hours thereafter. There were no significant differences in indicator population for samples
held at or below 10°C. At higher temperatures significant changes occurred in total plate count
and fecal coliform populations.
Mailing practices were investigated to determine if sample temperatures could be
maintained at or below 10°C for up to 30 hours. An 8x6x4¼” Styrofoam mailing kit packed with
2 15-oz ice bricks successfully maintained sample temperatures below 10°C when packages were
held at 25°C, but not 39°C. It is recommended that an 8x6x4¼” Styrofoam mailing kit
containing two 15-oz ice bricks should be used and that it should be labeled “Keep Package
Cold”. A temperature history indicator strip should be placed on the outside of the bottle
determine if temperatures exceeded 10oC during mailing. The kit should contain written
directions for growers on sample collection and handling practices, and a sample submission
form that directs the analytical laboratory to determine generic E. coli levels.
v
TABLE OF CONTENTS
LIST OF FIGURES……………………………………………………………………………..viii
LIST OF TABLES…………………………………………………………………………….…..x
CHAPTER 1. INTRODUCTION…………………………………………………………………1
1.1. STATEMENT OF THE PROBLEM…………………………………………………1
1.2. RESEARCH OBJECTIVES………………………………………………………….1
CHAPTER 2. LITERATURE REVIEW………………………………………………………….4
2.1. WATERBORNE PATHOGENS……………………………………………………..4
2.1.1. Contamination and outbreak sources in drinking and recreational water….4
2.1.2. Pathogenic Escherichia coli……………………………………………………..5
2.1.3. Salmonella spp……………………………………………………………...7
2.1.4. Campylobacter…………………………………………………………………9
2.1.5. Shigella spp…………………………………………………………………9
2.1.6. Yersinia enterocolitica……………………………………………………..10
2.1.7. Listeria monocytogenes……………………………………………………..10
2.1.8. Enteric Viruses………………………………………………………….…11
2.1.9. Protozoa………………………………………………………………...…12
2.2. THE USE OF INDICATOR ORGANISMS IN DETERMINING WATER
QUALITY………………………………………………………………………………..12
2.2.1. Total Plate Count………………………………………………………….14
2.2.2. Enterobacteriaceae………………………………………………………………15
2.2.3. Coliform…………………………………………………………………...15
2.2.4. Fecal Coliform…………………………………………………………….16
2.2.5. Escherichia. coli……………………………………………………………..16
2.2.6. Enterococci………………………………………………………………..17
2.3. METHODS FOR ENUMERATING INDICATOR ORGANISMS IN WATER
SAMPLES……………………………………………………………………………….17
2.3.1. Most Probable Number Techniques……………………………………….17
2.3.2. Plating Techniques………………………………………………………...18
2.4. PRODUCE SAFETY………………………………………………………………..22
2.4.1. Food Safety Risk from Produce…………………………………………...22
2.4.2. Produce Related Foodborne Illness Outbreaks……………………………26
2.5. GOOD AGRICULTURAL PRACTICES…………………………………………..29
2.6. IRRIGATION WATER AS A SOURCE OF PRODUCE CONTAMINATION…..30
2.6.1. Irrigation Water as a Vehicle for Contamination………………………….30
2.6.2. Risk Factors Affecting the Safety of Irrigation Water…………………….31
vi
2.7. UNIQUE RISKS ASSOCIATED WITH THE USE OF SURFACE WATER
FOR IRRIGATION……………………………………………………………………...34
2.7.1. Surface Water as a Vehicle for Contamination……………………………34
2.7.2. Foodborne Illness Outbreaks Linked to Contaminated Surface Water…...35
2.7.3. Microbial Standards For Irrigation Water……………………..…………..37
2.8. PROTOCOLS FOR WATER SAMPLING AND WATER SAMPLE
HANDLING……………………………………………………………………………..44
CHAPTER 3. SURVEY OF SURFACE WATER SOURCES USED FOR IRRIGATION
OF SPECIALTY CROPS IN PENNSYLVANIA……………………………………………….48
3.1. ABSTRACT…………………………………………………………………………48
3.2. INTRODUCTION………………………………………………………………..…49
3.3. MATERIALS AND METHODS………………………………………………...….50
3.3.1. Selection of Sampling Sites…………………………………………….…50
3.3.2. Collection and Transportation of Water Samples………………………...51
3.3.3. Measurements Obtained by Onsite Observation…………………………..51
3.3.4. Measurement of Physical Characteristics of Water Sources…………...…52
3.3.5. Microbial Analysis……………………………………………………..….53
3.3.6. Statistical Analysis……………………………………………………..….58
3.4. RESULTS……………………………………………………………………...……59
3.5. DISCUSSION AND CONCLUSIONS……………………………………………..70
CHAPTER 4. EVAULATION OF METHODS FOR DETECTING HUMAN
PATHOGENS IN SURFACE WATER SAMPLES………………………………………….…76
4.1. ABSTRACT……………………………………………………..………………….76
4.2. INTRODUCTION………………………………………………………………..…77
4.3. MATERIALS AND METHODS………………………………………………...….78
4.3.1. Selection and Preparation of Water Samples…………………………...…78
4.3.2. Salmonella Detection Methods……………………………………………78
4.3.3. E. coli O157:H7 Detection Methods………………………………………82
4.4. RESULTS…………………………………………………………………………...85
4.4.1. Salmonella spp. Detection Methods……………………………………....85
4.4.2. E. coli O157:H7 Detection Methods………………………………………87
4.5. DISCUSSION AND CONCLUSIONS………………………………………….….89
CHAPTER 5. TEMPERATURE ANALYSIS OF WATER SAMPLES MAILED
DURING SUMMER GROWING SEASON…………………………………………………….91
5.1. ABSTRACT…………………………………………………………………………91
5.2. INTRODUCTION…………………………………………………………………..91
5.3. MATERIALS AND METHODS…………………………………………………...91
5.4. RESULTS…………………………………………………………………………...92
vii
5.5. DISCUSSION AND CONCLUSIONS……………………………………………..94
CHAPTER 6. EFFECTS OF SAMPLE HOLDING TIME AND TEMPERATURE ON
MICROBIOLOGICAL POPULATIONS IN SURFACE WATER SAMPLES………………..95
6.1. ABSTRACT……………………………………………………………………..….95
6.2. INTRODUCTION…………………………………………………………………..95
6.3. MATERIALS AND METHODS…………………………………………………...96
6.3.1. Collection and Treatment of Surface Water Samples…………………….96
6.3.2. Microbial Analysis………………………………………………………...97
6.3.3. Analysis of Physical Characteristics……………………………………....98
6.3.4. Statistical Analysis………………………………………………………...98
6.4. RESULTS…………………………………………………………………………...99
6.5. DISCUSSION AND CONCLUSIONS……………………………………………108
CHAPTER 7. IDENTIFICATION OF MAILING PRACTICES TO CONTROL
MICROBIAL GROWTH IN A SURFACE WATER SAMPLE………………………………110
7.1. ABSTRACT………………………………………………………………………..110
7.2. INTRODUCTION………………………………………………………………....110
7.3. MATERIALS AND METHODS……………………………………………….….111
7.4. RESULTS…………………………………………………………………….……113
7.5. DISCUSSION AND CONCLUSIONS……………………………………...…….116
CHAPTER 8. SUMMARY AND CONCLUSIONS……………………………………...……118
CHAPTER 9. FUTURE RESEARCH………………………………………………………….122
REFERENCES…………………………………………………………………………………124
viii
LIST OF FIGURES
FIGURE 2.1. Causes of drinking water outbreaks in the United States, 1971-2008……………..4
FIGURE 2.2. Venn diagram showing the relationship between common indicator
organisms and human waterborne pathogens…………………………………………………....14
FIGURE 2.3. Causes of illness in 1565 single food commodity outbreak, 2003-2008…………23
FIGURE 2.4. Average number of illnesses per outbreak in single food commodity
outbreaks, 1998-2005…………………………………………………………………………….25
FIGURE 2.5. Commodities implicated in produce related foodborne illness outbreaks
in the United States, 1998-2006………………………………………………………………….28
FIGURE 3.1. Location of farms from which surface water samples were collected
during 2010 and 2011…………………………………………………………………………....50
FIGURE 3.2. Results of total plate count analysis at each sampling location……………….…63
FIGURE 3.3. Results of Enterobacteriaceae analysis at each sampling location………………63
FIGURE 3.4. Results of total coliform analysis at each sampling location…………………….64
FIGURE 3.5. Results of fecal coliform analysis at each sampling location………………….…64
FIGURE 3.6. Results of E. coli analysis at each sampling location…………………………….65
FIGURE 3.7. Results of enterococci analysis at each sampling location……………………….65
FIGURE 3.8. Percent of surface water samples collected which failed current surface
water microbial standards……………………………………………………………………..…66
FIGURE 3.9. Average bacterial levels in water sources at each level of water
movement (a), algae growth (b), and sunlight (c)……………………………………………….70
FIGURE 4.1. Methods for detecting the presence of Salmonella in surface water samples……81
FIGURE 4.2. Methods for detecting the presence of E. coli O157:H7 in water samples………84
ix
FIGURE 5.1. Temperature profiles of water samples mailed from Wyomissing via
USPS Express Post to University Park on June 28-29, 2011 without coolant (a) and
with a flexible gel ice sheet (b)…………………………………………………………………..93
FIGURE 5.2. Temperature profiles of water samples mailed from Wyomissing via
USPS Express Post to University Park on July 21-22, 2011 without coolant (a) and
with a flexible gel ice sheet (b)…………………………………………………………………..93
FIGURE 5.3. Temperature profiles of water samples mailed from Wyomissing via
USPS Express Post to University Park on August 1-2, 2011 without coolant (a) and
with a flexible gel ice sheet (b)…………………………………………………………………..93
FIGURE 6.1. Log change in the total plate count of surface water samples held at 5
different temperatures…………………………………………………………………………..103
FIGURE 6.2 Log change in the coliform counts of surface water samples held at 5
different temperatures………………………………………………………………………..…103
FIGURE 6.3 Log change in the fecal coliform counts of surface water samples held
at 5 different temperatures……………………………………………………………………...104
FIGURE 6.4 Log change in the E. coli counts of surface water samples held at 5
different temperatures…………………………………………………………………………..104
FIGURE 6.5 Log change in the Enterococci counts of surface water samples held at
5 different temperatures……………………………………………………………………...…105
FIGURE 7.1. Temperature profiles of water samples 1, 2, 3, and 4, held at 39°C for
30 hours…………………………………………………………………………………………113
FIGURE 7.2. Temperature profiles of water samples 5, 6, 7, and 8, precooled to
refrigeration temperatures and held at 39°C for 30 hours…………………………………...…114
FIGURE 7.3. Temperature profiles of water samples 9, 10, 11, and 12, held at 25°C
for 30 hours……………………………………………………………………………………..115
FIGURE 7.4. Temperature profiles of water sample 13 replicates held at 25°C for
30 hours…………………………………………………………………………………………116
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LIST OF TABLES
TABLE 2.1. Causes of produce-related foodborne outbreaks in the United States,
1973 to 1997……………………………………………………………………………………..27
TABLE 2.2. List of current government standards for the use of recreational or
surface water……………………………………………………………………………………..38
TABLE 2.3. List of current industry and commodity group standards for the use
of recreational or surface water………………………………………………………………..…39
TABLE 3.1. Definitions of terms used to describe characteristics of water sources…………....52
TABLE 3.2. Primers and PCR conditions used to confirm pathogen presence………………....57
TABLE 3.3. Characteristics of water sources sampled in 2010 and 2011………………………60
TABLE 3.4. Descriptive statistics for microbial indicator organisms and physical
characteristics…………………………………………………………………………………….62
TABLE 3.5. Comparison of indicator organism levels to current surface water standards
in those samples which tested positive for Salmonella…………………………………………..67
TABLE 3.6. Pearson correlations between microbial indicator organisms measured…….…….68
TABLE 3.7. Pearson correlations between microbial indicator organisms and physical
characteristics measured in the study………………………………………………………….…69
TABLE 4.1. Initial total plate counts for uninoculated surface water samples………………....85
TABLE 4.2. Number of surface water samples at each inoculation dilution in which
confirmed Salmonella was found using each detection method………………………………....86
TABLE 4.3. Number of surface water samples at each inoculation dilution in which
confirmed E. coli O157:H7 was found using each detection method…………………………....88
TABLE 5.1. Daily high and low temperatures in Wyomissing and State College, PA
during overnight mailing studies………………………………………………………………...92
xi
TABLE 6.1. Initial bacterial levels and physical characteristics of surface water
samples collected for the temperature holding study…………………………………………….99
TABLE 6.2. Results of the analysis of water samples used in the holding study
obtained from the Penn State Agricultural Analytical Laboratory……………………………..101
TABLE 6.3. Average change in microbial levels from time 0 after 6, 18, and 30 hours,
as obtained by paired t-tests……………………………………………………………….……106
TABLE 6.4. Results for water samples used for holding study when microbial levels
from time 0 and time 30 were compared to current surface water standards…………………..107
TABLE 7.1. Packing, Coolant, and Storage Conditions used for each water sample…………112
1 CHAPTER 1. INTRODUCTION
1.1 STATEMENT OF THE PROBLEM
Contamination from surface water used for irrigation has been shown to be the cause of
produce-associated foodborne illness outbreaks. The use of surface water for irrigating fruit and
vegetable crops is increasing in the United States, so the risk of contamination by irrigation water
is rising. Currently, little is known about the microbiology of surface water irrigation sources and
how growers can determine if the water they use for produce production is safe.
1.2. RESEARCH OBJECTIVES
The overall objective of this research was to investigate the microbiology of Pennsylvania
surface waters used for irrigation water. Specific research objective of this project were:
1. Survey surface waters in Pennsylvania to determine the current levels of pathogenic
and non-pathogenic bacteria in the Pennsylvania water supply.
Very little is known about the microbiology of surface waters in Pennsylvania. Studying
indigenous microbial populations will help to understand food safety risks of surface
water sources and will provide necessary information for further research.
2. Identify correlations between indicator organisms, physical characteristics and
pathogen presence in surface water.
Conflicting information exists on the ability of indicator organisms to predict pathogens
in a surface water source. Determining if a relationship exists between these types of
organisms and understanding this relationship can help to evaluate the safety of surface
2 water sources for use in food production. Relationships between physical characteristics
and microbiological levels can help to identify the best times for water sampling, or may
provide characteristics which can be quickly and easily screened to identify high risk
water sources.
3. Investigate the ability of current surface water standards to predict the safety of a water
source.
Currently, microbiological standards for the use of surface water in irrigating crops have
been established by some commodity groups; however, none of these standards have
been scientifically validated to prove that adhering to these standards ensures that the
water is safe to use. Evaluating these current standards through sampling surface water
irrigation sources will help determine whether they actually predict food safety risks.
4. Determine how overnight mailing affects sample temperature and therefore changes in
indicator organism populations.
Current standard methods require that surface water samples be tested within six hours of
collection due to surface water’s ability to support rapid changes in microbial levels.
Research into the effects of long holding times on the microbiology of surface water
samples has been inconclusive. This research aims to identify temperature storage
conditions under which the microbiological populations of a sample will be maintained
for up to 30 hours.
3 5. Identify mailing practices which will maintain a water sample at an appropriate
temperature to control microbial change throughout mailing.
Packaging criteria will be investigated to determine a method for mailing surface water
samples that will minimize microbial changes in surface water samples for up to 30
hours.
6. Create a mail-in surface irrigation water sampling kit in conjunction with the Penn
State Agricultural Analytical laboratory and distribute to Pennsylvania growers.
Results of all previous objectives will be used to create a prototype of a water testing kit
which can be used by Pennsylvania growers to mail surface water for testing of microbial
indicators.
4 CHAPTER 2. LITERATURE REVIEW
2.1. WATERBORNE PATHOGENS
2.1.1. Contamination and outbreak sources in drinking and recreational water
Waterborne pathogens are bacteria, viruses, and protozoa that survive or grow in water
and cause illness in humans (Ferguson et al. 2003). Water sources can become contaminated by
pathogenic microorganisms through the introduction of animal or human fecal contamination,
either by direct introduction into rivers, streams, lakes, or ponds or by run-off from the land into
the groundwater system. Human infection by pathogens can occur as a result of malfunctioning
water treatment systems, during recreational water activities, and by eating foods grown or
washed with contaminated water (Hsu et al. 2011).
Outbreaks of waterborne diseases have been documented in the United States since the
1920s (APHA 2005). Summaries of drinking water outbreaks in the United States between 19712008, show that bacteria, followed by viruses, chemicals, and parasites, are the most commonly
implicated causes when a source is identified (Figure 2.1) (Craun et al. 2010; Brunkard et al.
2011).
FIGURE 2.1. Causes of drinking water outbreaks in the United States, 1971-2008 (Data from
Craun et al. 2010; Brunkard et al. 2011).
5 2.1.2. Pathogenic Escherichia coli
Escherichia coli (or E. coli) is a species of Gram negative, rod-shaped, nonsporeforming, facultative anaerobic bacteria of fecal origin within the Enterobacteriaceae family
(EPINET 2011). These organisms are part of the normal intestinal microflora of humans and
many other animals. Most strains of E. coli do not cause illness in humans; however, some are
able to produce verotoxins which can cause serious and even life threatening illness (Alsanius
2010). Pathogenic E. coli are classified two ways, by serotype (antigenic differences) and by
virotype (virulence factors) (APHA 2001b). Serotyping of E. coli relies on the identification of
two surface components of the bacterial cell, the O antigen from the lipopolysaccharide and the
H antigen from the flagella (APHA 2001b). Based on the serological type of each of these
components, a strain is designated with an O number and an H number (for example E. coli
O157:H7) which together make up the strain’s serotype (APHA 2001b). Virotypes are based on
the ability to attach to host cells, effects of attachment on the host cell, toxin production, and the
degree of invasiveness (APHA 2001b). Diarrheagenic E. coli fall into five different virotypes,
Enterotoxigenic E. coli (ETEC), Enteroaggregative E. coli (EAggEC), Enteropathogenic E. coli
(EPEC), Enterohemorrhagic E. coli (EHEC), and Enteroinvasive E. coli (EIEC) (Doyle and
Beuchat 2007).
The ability of some E. coli strains to cause severe illness in humans is due to the
production of shiga toxins (Nwachuku and Gerban 2008). Shiga toxin-producing E. coli (STEC
E. coli) cause approximately 175,000 cases of illness in the United States each year leading to
more than 2,000 hospitalizations and 20 deaths (Scallan et al. 2011). Pathogenic E. coli can
cause a wide variety of symptoms in people ranging from mild diarrhea to hemorrhagic colitis,
which is characterized by profuse and bloody diarrhea, and in very severe cases hemolytic
6 uremic syndrome (HUS) that causes bloody diarrhea followed by renal failure, thrombocytopenic
purpurea, and central nervous system symptoms (Alsanius 2010).
Escherichia coli O157:H7 is one of the most commonly implicated serotypes of E. coli
that cause human illness (Nwachuku and Gerban 2008). It is an enterohemmoraghic E. coli
which can cause potentially fatal HUS (FDA 2002a). It was first identified as a human pathogen
in 1982 after an outbreak of foodborne illness linked to the consumption of cooked ground beef
(Riley et al. 1983); however, it wasn’t until a 1993 multistate outbreak of E. coli O157:H7 in
undercooked ground beef patties purchased from a fast-food chain that it became widely
recognized as an important human health risk (Rangel et al. 2005). This organism is responsible
for approximately 73,480 illnesses, 2,168 hospitalizations, and 61 deaths each year in the United
States, and is noted as an important cause of renal failure in children (Neill et al. 1987; Siegler et
al. 1994). Some of the reasons why E. coli O157:H7 causes so many illnesses are that it requires
a very low infectious dose of between 10 and 100 cells and it has the ability to withstand harsh
conditions including low pH, temperature, and water activity (Alsanius 2010). The natural
reservoir of E. coli O157:H7 is in the gastrointestinal tract of livestock, wild and domesticated
animals, and birds (Nwachuku and Gerban 2008; Alsanius 2010).
Surveys of surface waters in the United States have reported E. coli O157:H7
contamination of water sources throughout the country (Armstrong et al. 1996; Olsen et al. 2002;
Gannon et al. 2004). When water is contaminated by manure containing E. coli O157:H7,
bacterial cells can survive in the water for up to 92 days (McGee et al. 2002). Survival in water is
affected by temperature, sunlight, and nutrient levels (Cooley et al. 2007). In a study of one
surface water system, survival was shown to be greater at lower temperatures and when the
organism was attached to sediments within the water source (Czakowska et al. 2005).
7 E. coli O157:H7 is most frequently linked to outbreaks associated with foods of animal
origin (Abdul-Raouf et al. 1993); however, between 1982 and 2002, waterborne outbreaks
accounted for 9% of all E. coli O157:H7 outbreaks in the United States (Rangel et al. 2005). In
2000, an outbreak of E. coli O157:H7 attributed to a municipal drinking water source in
Walkerton Ontario sickened more than 2500 town residents and killed at least seven people
(Schraft and Watterworth, 2004). As a direct result of this tragedy, surveillance of the microbial
quality of surface and drinking water was increased in developed nations (Schraft and
Watterworth, 2004).
2.1.3. Salmonella spp.
Salmonella spp. is a facultatively anaerobic, Gram negative, rod shaped, enteric pathogen
of the Enterobacteriaceae family that is ubiquitous in the environment (Levantesi et al. 2011). It
is one of the leading causes of intestinal illness in the world and is the etiological agent of
typhoid and paratyphoid fevers (Pond 2005). There are more than 2500 recognized Salmonella
serovars each characterized by host specificity and distribution (Alsanius 2010; Levantesi et al.
2011). The genus Salmonella comprises two species, Salmonella bongori and Salmonella
enterica (Levantesi et al. 2011). Salmonella enterica can be divided into six subspecies (enterica,
salamae, arizonae, diarizonae, indica, and houtenae), of which Salmonella enterica subspecies
enterica is mainly associated with warm blooded vertebrates including humans (Levantesi et al.
2011). Salmonella sources in agricultural environments include domesticated and wild animals,
birds, reptiles, plants, insects, and infected food handlers (Levantesi et al. 2011).
Salmonella can be classified into two separate groups, typhoidal serovars and nontyphoidal serovars, based on the clinical symptoms they cause. Typhoidal serovars, cause
8 typhoid and paratyphoid fevers and are responsible for approximately 17 million cases of illness
worldwide each year (Kindhauser 2003). Typhoidal serovars are host-adapted and therefore can
only infect humans and are transmitted by human fecal contamination (Levantesi et al. 2011).
Unlike Salmonella Typhi and Paratyphi, non-typhoidal Salmonella serovars are found in many
human and animal species so transmission can stem from fecal contamination by infected
humans or animals (Levantesi et al. 2011). Non-typhoidal Salmonella serovars cause acute, selflimiting illnesses in the majority of the population, but these illnesses can become very serious in
children, the elderly, and other immunocompromised populations (Levatesi et al. 2011). The
USDA estimates that 696,000-3,840,000 illnesses and between 870 and 1,920 deaths are
attributed to non-typhoidal Salmonella serovars each year (APHA 2001b). Non-typhoidal
Salmonella can cause nausea, vomiting, abdominal cramps, diarrhea, fever, headache and
arthritic symptoms (FDA 2012). Similar to E. coli O157:H7, non-typhoidal serovars of
Salmonella can have low infectious doses of approximately 100 cells (Blaser and Newman
1982).
Studies on Salmonella spp. in the environment have shown that it remains viable in
freshwater longer than many other enteric bacteria (Chao et al. 1987; Wright 1989). Sediments
associated with fresh water form a micro-ecological niche which can harbor and therefore
enhance the survival of Salmonella spp. in rivers and lakes (Chandran et al. 2011).
Although Salmonella spp. are commonly described as a foodborne pathogen, recent
outbreaks of salmonellosis have been linked to both drinking and recreational water (Van Houten
et al. 1998; Boase et al. 1999; Leclerc et al. 2002; Mohle-Boetani et al. 2002; Ashbolt 2004), and
it is considered the leading cause of waterborne disease outbreaks (Kramer et al. 1996).
9 2.1.4. Campylobacter spp.
Campylobacter is a large genus of Gram negative, highly motile, curved or spiral shaped,
microaerophilic bacteria, which encompasses 17 different species (Alsanius, 2010). It is the most
common cause of gastrointestinal illnesses worldwide (Evens et al. 2003). Campylobacter jejuni,
C. coli, and C. lari are harbored in the intestinal tract of warm blooded animals including birds,
domestic animals, and rodents and are therefore are most often transmitted via fecal
contamination (Wahlstrom et al. 2003). Symptoms of Campylobacter spp. infections range
symptoms from profuse, dysentery-like, watery diarrhea to bloody diarrhea containing mucus
and white blood cells (APHA 2001b). Although Campylobacter is considered a foodborne
pathogen, it has been isolated from water, feces, soil, plants, and other environmental samples
(Alsanius, 2010). Campylobacter has been shown to be one of the most common causes of
waterborne disease in the United States (Andersson and Bohan 2001).
2.1.5. Shigella spp.
The genus Shigella contains species of non-motile, oxidase negative, Gram negative rods,
which are differentiated from other enteric bacteria by their inabaility to ferment lactose (Doyle
and Beuchat 2007). Shigella spp., like E. coli and Salmonella spp., are members of the
Enterobacteriaceae family, which are nearly genetically identical to E. coli, and closely related
to Salmonella spp. (APHA 2001b). Although genetically similar, Shigella can be differentiated
from E. coli by their high invasiveness and slower metabolism (APHA 2001b). Shigella is
comprised of 4 subspecies which are differentiated by their O antigen: S. dysenteriae, S. flexneri,
S. boydii, and S. sonnei (Alsanius 2010). Shigella is the cause of bacterial dysentery which
results in bloody mucosal diarrhea, abdominal cramps, fever, and tenesmus (Doyle and Beuchat
10 2007). Because humans and higher primates are the only known hosts of Shigella spp.,
transmission typically occurs through human fecal contamination of water or foods (Alsanius
2010).
2.1.6. Yersinia enterocolitica
Yersinia enterocolitica is a Gram negative, coccobacillus-shaped human pathogen which
is known to reside in water sources, particularly in the cool or temperate regions of the United
States (APHA 2005). This is probably true because Yersinia can grow at temperatures as low as
4°C (APHA 2005). Many wild animals with water-associated habitats (e.g. beavers, muskrat,
otters, and raccoons) are known to be reservoirs for Yersinia enterocolitica (Wetzler and Allard
1977). This species has been isolated from surface waters around the United States; however,
little information exists on the survival of Yersinia in surface waters or during water treatment
(APHA 2005). Yersinia enterocolitica infection in humans causes yersiniosis, symptoms of
which yersiniosis vary by the age of the victim, and may include gastroenteritis, diarrhea,
vomiting, fever, and abdominal pain which mimics appendicitis (FDA 2011).
2.1.7. Listeria monocytogenes
Listeria monocytogenes is a Gram positive, catalase positive, rod shaped bacillus species
(Doyle and Beuchat 2007). This pathogen has been a well-established cause of meningitis and
perinatal septicemia in the US since 1935, although it remained relatively obscure to the general
public until the mid 1980s (APHA 2001b). Listeria monocytogenes is a very resilient organism
growing over a temperature range from 1 to 45°C, pH values between 4.4 and 9.6, at water
activities as low as 0.9, and in foods or media containing up to 10% NaCl (Alsanius 2010). It is
11 unique among pathogens in that it can exist in a saprophytic state which allows for long term
survival outside of a human or animal host (Wen et al. 2011). Listeriosis is a highly invasive
illness for individuals in high risk groups including the elderly, children, and the
immunocompromised (APHA 2001b). Symptoms of listeriosis include fever, malaise, ataxia,
seizures, altered mental status, endocarditis, septic arthritis, osteomyelitis, premature delivery,
spontaneous abortion, and stillbirth (APHA 2001b). Listeriosis may take months to develop after
exposure and can therefore be very difficult to trace back to an outbreak source (APHA 2001b).
Pathogenic and non-pathogenic species of Listeria spp. are widely distributed in the environment
including in soil, mud, silage, decaying vegetation, water, sewage, and feces (Doyle and Beuchat
2007) and have been isolated from many mammals, birds, fish, amphibians, and insects (APHA
2001b). Since Listeria monocytogenes is ubiquitous in the environment, it is easily transmitted to
water sources. Despite its ubiquity, a high minimal infectious dose (approximately 10,000) limits
the number of outbreaks (Alsanius 2010).
2.1.8. Enteric Viruses
Viruses differ from bacteria in that they require a living host cell to replicate (Alsanius
2010). They can survive for longer periods of time in extreme conditions, including cold water
sources (Alsanius 2010). Enteric viruses are excreted by infected humans, so contamination of a
water source can occur via human fecal contamination (Alsanius 2010). Two of the most
common viruses implicated in waterborne illness outbreaks are Hepatitis A and Norovirus
(Alsanius 2010). Symptoms of Norovirus include gastroenteritis and vomiting, whereas Hepatitis
A causes inflammation of the liver leading to fatigue, fever, abdominal pain, nausea, jaundice,
and loss of bile (Alsanius 2010).
12 2.1.9. Protozoa
Protozoa are single celled parasites which proliferate by cysts or oocysts in the intestinal
tract of animals and are excreted with feces (Alsanius 2010). Two of the most common protozoa
which cause waterborne illness in the United States are Cryptosporidium sp. and Giardia sp.
both of which cause a self-limiting illness of diarrhea and vomiting, but this illness can be lifethreatening in immunocompromised individuals (Alsanius, 2010). Protozoa are particularly
dangerous to humans because of the low infectious dose required, as few as 9 oocytes (Okhuysen
et al. 1999), and the fact that most common water disinfection methods (chlorination and
ultraviolet radiation) may not adequately destroy these organisms (Alsanius 2010).
2.2. THE USE OF INDICATOR ORGANISMS IN DETERMINING WATER QUALITY
Indicator organisms are not human pathogens; however, their presence in water suggests
the probable presence of a pathogen (Griffin et al. 2001). Testing for pathogens in water is
difficult because levels able to cause illness may be too low to be detected by standard methods.
Testing is also very expensive, time consuming, and requires specially trained personnel and
equipment. Because of these difficulties, water quality is most often assessed using indicator
organisms (Harwood et al. 2004). Fecal indicator organisms are found in the intestinal tract of
animals or humans and are therefore shed through feces (Griffin et al. 2001).
The use of indicator organisms for assessing water quality dates back as far as the 19th
century (Griffin et al. 2001). Initial research into water quality began at the Massachusetts State
Board of Health and Massachusetts Institute of Technology (Griffin et al. 2001). This early water
quality research, performed by Theodor Escherich, labeled E. coli a marker of fecal
13 contamination in water, and ever since then the coliform group of bacteria have been used in
water testing.
There has been no measure of microbial contamination in water that has been universally
accepted to reflect the risk levels associated with water sources (Griffin et al. 2001). Many
indicator organisms have been suggested; however, none have been identified which are suitable
for all regions and in all waters types (Griffin et al. 2001). According to Griffen et al. (2001),
Caplenas and Kanarek (1984), EPINET (2011), and Sinclair et al. (2012), the ideal indicator
organism has the following characteristics:

Is always present when pathogens are present

Occurs primarily in the intestinal tract of humans and animals

Is not as fastidious as pathogens/survives a little longer in water than pathogens

Is more resistant to disinfection treatments than pathogens

Is easy to isolate and enumerate

Can be isolated from both saline and fresh waters

Can be used to indicate the presence of multiple pathogens

Is present in higher numbers than pathogens, and thus allows for detection even when
pathogens are at undetectable, but still dangerous levels

The concentration of the organism is related to the degree of fecal contamination of a
water source

There is an established correlation between the level of the organism and the health risk
of a water source
The relationship of many of these indicator organisms and human pathogens is shown in
Figure 2.2. In this figure, all bacteria listed inside of a circle are a subset of those within the next
larger circle(s) in which it is enclosed. For instance, fecal coliforms are a subset of coliform
14 bacteria, and although all fecal coliform will show up on a coliform test, not all coliforms will
show up on a fecal coliform test.
FIGURE 2.2. Venn diagram showing the relationship between common indicator organisms and
human waterborne pathogens
Some types of bacteria used as indicator organisms are outlined below.
2.2.1. Total plate count
Total plate count is an enumeration of all mesophilic aerobic microorganisms in a water
sample (APHA 2005) and therefore is used as an indicator of the total level of microbial
contamination of a water source. Total plate count is not often used as an indicator of water
15 quality since it does not differentiate between harmless bacteria which are indigenous to a water
source and those bacteria that occur as a result of fecal contamination. As such, it does not give a
clear picture of risk to humans from a water source. However, total plate count can be used as a
method of quickly assessing changing levels of microbial contamination of a water source.
2.2.2. Enterobacteriaceae
Enterobacteriaceae are a large family of Gram negative, rod-shaped, facultative
anaerobic bacteria which includes many of the common indicator organisms as well as many
pathogens, including Salmonella spp., E. coli O157:H7, Yersinia spp., and Shigella spp.
Enterobacteriaceae are not a common water quality indicator due to the wide range of organisms
contained in this family.
2.2.3. Coliforms
Coliforms are Gram negative, non-sporeforming, rod-shaped aerobes and facultative
anaerobes which ferment lactose to form gas and acid at 35-37°C within 48 hours (APHA 2005).
This group of bacteria is defined by biochemical properties and growth characteristics instead of
taxonomy. As such, they include bacteria of many genera including Escherichia, Klebsiella, and
Serratia (Schraft and Watterworth 2004). Although this group has long been used as an
indicator, there are some drawbacks to this choice. Coliforms include many harmless bacterial
species that are indigenous to soil and which can be passed easily to water from soil in an
agricultural environment and therefore would not represent fecal contamination. Additionally,
there are some indigenous bacteria in marine waters (such as pseudomonads and vibrios) which
16 can mimic coliforms using traditional media-based enumeration methods, although they are not
species typically associated with soil-derived coliforms (Griffin et al. 2001).
2.2.4. Fecal coliforms
Fecal coliforms are a subset of coliform bacteria which ferment lactose to form acid and
gas between 44.5 and 45.5°C (EPINET 2011). In recent years this group of bacteria has more
accurately been termed thermotolerant coliforms. This is a more appropriate term because they
differ from total coliforms by their higher optimal growth temperature and not necessarily by
their origin. Fecal coliforms are differentiated from other coliforms in the laboratory by
incubating media at 44°C instead of the 35-37°C used to enumerate total coliforms. Most human
pathogens are enteric bacteria that enter the body via the fecal-oral route. Since pathogens are
transmitted through the feces of humans and animals, fecal coliforms may serve as a better
indicator of human pathogens than coliforms (Griffin et al. 2001); however, since some
coliforms that do not originate in the intestinal tract of mammals (including Klebsiella,
Enterobacter, and Citrobacter) can grow at this higher temperature, their presence may
overestimate the number of bacteria of fecal origin reported by the fecal coliform test (Schraft
and Watterworth 2004).
2.2.5. Escherichia coli
Escherichia coli (E. coli) is one species of Gram negative, rod-shaped, facultative
anaerobic, non-sporeforming enteric bacteria within the fecal coliform group (Doyle and Beuchat
2007). It is the predominant facultative anaerobe in the human bowel and is an essential part of
the intestinal microflora required for maintaining human health (FDA 2002a). Because E. coli
17 strongly indicates the occurrence of fecal contamination, it is widely recognized as the most
appropriate indicator organism for monitoring the microbial quality of drinking water (World
Health Organization 1993). There are some strains of E. coli, including E. coli O157:H7, which
can be pathogenic to humans; however, the presence of generic E. coli is not absolute evidence
of the potential for causing illness since the majority of E. coli strains are non-pathogenic.
2.2.6. Enterococci
Enterococci is a genus of bacteria which naturally reside in the intestinal tract of humans
(APHA 2001a). Enterococci are the only indicator organisms discussed in this section which are
not a part of the Enterobacteriaceae family. It has been suggested that enterococci are a better
indicator of human fecal contamination than the Enterobacteriaceae family because they are
more closely associated with human fecal matter rather than animal fecal matter, and they can
survive in waters for longer periods of time (Griffin et al. 2001).
2.3. METHODS FOR ENUMERATING INDICATOR ORGANISMS IN WATER
SAMPLES
Standard methods have been developed for the microbial analysis of water and by the
American Public Health Association. These standards are compiled in The Standard Methods for
Water and Wastewater Analysis (APHA 2005). These are the methods used by water testing
laboratories for the analysis of water samples.
2.3.1. Most probable number techniques
One standard method for enumerating indicator organisms in water is the most probable
number (MPN) technique. MPN is a statistical method which uses the presence or absence of
18 organisms in serial dilutions to estimate the level of bacteria in a sample (APHA 2001a).
Because these methods do not provide a direct measure of the bacterial count of a sample, results
are more variable compared to plating methods (APHA 2001a). Colilert™ and Colisure™ are
modified MPN methods developed specifically for the identification of coliforms and E. coli in a
sample (IDEXX 2012). These methods differ only in their detection limits and cost per sample.
In both of these procedures, a water sample is mixed with a proprietary mixture of enzymes
which react with coliforms and E. coli to produce a color change. The mixture is then poured
into a sample tray containing multiple wells, and the numbers of wells in which a color change
occurs is used to statistically determine the level of both coliforms and E. coli in the sample.
Wells which contain coliforms turn from clear to yellow, and those which contain E. coli are
detectable by fluorescence (IDEXX 2012).
2.3.2. Plating Methods
Plating techniques for indicator organisms include three common methods of analysis:
direct plating, membrane filtration, and Petrifilm™ methods.
Direct Plating
Direct plating methods involve the culturing of bacteria on solid agar plates which
contain specific nutrients (APHA 2001). There are two types of direct plating methods, spread
plating and pour plating. Spread plating involves the dispersion of a small volume of liquid
sample on top of a solid agar plate. In contrast, a pour plate involves the mixing a volume of
liquid sample into liquid agar and allowing this to solidify upon on cooling. Pour plates cause
bacterial cells to mix throughout the agar and thus result in a more anaerobic environment. These
19 methods rely on the assumption that, given appropriate growth conditions and nutrients, each
individual organism will grow to create a visible separate colony (APHA 2001). Agar plate
media can include various combinations of carbohydrate sources to provide carbon, proteins or
amino acids that provide nitrogen and sulphur, water, a source of lipids and fatty acids, salts for
growth and pH management, vitamins, and antimicrobial, selective, or differential agents (Reddy
et al. 2007). Inoculated agar plates are incubated at optimal growth conditions for specific
organisms including, oxygen level, temperature, and humidity. Direct plating can be selective for
specific organisms by choosing nutrient mixtures and growth conditions that select against
certain bacteria. They can also be used to differentiate microbial types by including compounds
that result in differences in colony appearance.
Membrane filtration
Another standard method for enumerating indicator bacteria in water samples is by
membrane filtration. Membrane filtration is useful for concentrating bacteria in samples with low
initial bacteria levels. In this method, water is vacuum-filtered through a membrane which is
subsequently placed on solid media and then incubated at an organism’s optimal growth
temperature (Schraft and Watterworth 2004). Pores within the membranes allow water to pass
through while bacteria are trapped on the membrane. Bacteria entrapped on the membrane utilize
the nutrients in the agar in the same manner as a liquid sample that has been spread plated. The
membrane filtration method is time consuming and requires expensive membranes compared to
direct plating methods (Schraft and Watterworth 2004). In addition, when the water sample has
high levels of background microflora, competition for nutrients from the agar may limit the
growth of the organism that is being enumerated; however, dilutions to reduce the levels of
20 background microflora may also dilute the target organism to below enumerable levels (Mattelet
2005). Moreover, sediment that becomes trapped in the membrane may provide a source of
nutrients not normally present in selective media and thus may alter the results of the test
(Mattelet 2005).
Petrifilm™
Because of the time and expense associated with the standard methods of enumerating
indicator organisms, newer methods have been developed and tested for use in water quality
monitoring. One of these methods is 3M Petrifilm™, a ready-made-film based medium (3M
2012). Petrifilm™ methods were originally developed and are considered standards methods for
enumerating microorganisms in the food and dairy industries (Curiale et al. 1991; AOAC 2000a;
AOAC 2000b; Priego et al. 2000; Russell 2000); however, they have yet to be added to the
standard methods for water analysis (APHA 2005). Petrifilm™ methods are desirable for water
testing because they are inexpensive, require very little equipment, are simple and fast to use
with little training, have a long shelf life, and have been proven to be reliable (Vail et al. 2003;
Schraft and Watterworth 2004; Mattelet 2005; Stepenuck 2010).
Environmental water sampling studies have been performed to compare Petrifilm™
results to those obtained through standard methods. The most studied waterborne bacteria
enumerated using Petrifilm™ is E. coli. Studies to determine E. coli in surface water have found
no significant differences between counts obtained on Petrifilm™ and by membrane filtration
(Vail et al. 2003; Mattelet 2005; Schraft and Watterworth 2005). Some studies investigated the
correlation between E. coli results from Petrifilm™ and MPN methods, including newer MPN
methods such as Colilert™. A correlation of 1 would indicate a direct one to one relationship in
21 the levels of E. coli detected using each method. These studies showed that E. coli levels
obtained using both tests were significantly correlated (p ≤ 0.01) of 0.9 and 0.93 (Beloti et al.
2003; Vail et al. 2003). In a study which used reference strains of known concentration to
compare results of E. coli obtained by membrane filtration, original MPN, Colilert™,
Colisure™, pour plate, spread plate, and Petrifilm™, the most consistent and accurate results
were obtained using Petrifilm™ and the pour plate method (Wholsen et al. 2005). Finally, a
study which compared E. coli results obtained from Petrifilm™, original MPN method,
Colilert™, Colisure™, and membrane filtration reported that Petrifilm™ had the highest
specificity (90.9%) (Horman and Hanninen 2006). Coliform counts from Petrifilm™ have also
been compared to membrane filtration and MPN in two studies, and in both, a significant (p ≤
0.01) correlation of 0.9 was reported between the two methods (Beloti et al. 2003; Mattelet
2005). In other studies, when fecal coliform results obtained using Petrifilm™ were compared
with membrane filtration results, the tests had a significant (p ≤ 0.05) correlation value of 0.95
(Mattelet 2005; Schraft and Watterworth 2005). Finally, when aerobic plate counts from marine
samples were investigated, it was found that Petrifilm™ results had a correlation value of 0.98 (p
≤ 0.05) when compared to conventional agar plating methods established for enumerating marine
microorganisms (Kudaka et al. 2009). This study also reported that the tests were equally
sensitive when equivalent volumes of water were used for testing.
Although Petrifilm™ produced equivalent results to the other methods discussed at high
initial levels of bacteria, in samples which contained very low levels of E. coli or fecal coliform,
Petrifilm™ has a lower sensitivity than other methods due to small volume of sample used in
testing (Vail et al. 2003; Schraft and Watterworth 2005; Horman and Hanninen 2006).
22 Currently, some surface water quality testing in progress in Midwestern U.S. states are
performed by volunteers with limited training and very little technical background. A study of
the monitoring practices of these groups concluded that Petrifilm™ is a good choice for testing
water by volunteers because of its ease of preparation and use and good correlation to standard
methods (Stepenuck et al. 2010). These findings have been published in the national EPA
volunteer monitoring newsletter (O’Brien 2006). Although Petrifilm™ is not yet a standard
method for water analysis, it is gaining popularity and has already been used for some water
quality research studies including a study of fresh and coastal waters in Ireland (Shakalisava et
al. 2010) and a study of water bodies in the Adirondack forest (McEwen et al. 2011).
2.4. PRODUCE SAFETY
2.4.1. Food safety risks from produce
Increasing produce safety is important for both growers and consumers. Declining
consumer confidence in fresh produce can lead to serious economic hardships for producers and
processors and also undermine public health initiatives aimed at increasing fresh fruit and
vegetable intake (Suslow 2010).
Historically, foodborne illness has been associated in the minds of the public with meat
and animal products (Abdul-Raouf et al. 1993); however, there is evidence for contamination of
produce crops as far back as 1912 when an outbreak of Salmonella enterica serovar Typhi
(typhoid fever) was linked to vegetables (Creel 1912).
In recent years, an increasing number of foodborne illnesses have been linked to
consumption of fresh fruit and vegetables (Tauxe et al. 1997; Sivapalasingam et al. 2004). A
2011 report on foodborne illnesses outlined the percentage of illnesses attributed to each single
23 commodity source (CDC 2011). Between 1990 and 2005, there were at least 713 outbreaks
traced to produce consumption (CSPI 2008). Figure 2.3 shows the food sources for all foodborne
illness outbreaks. Leafy greens alone cause the second highest number of outbreaks of any plant
or animal food, second only to poultry (CDC 2011). When outbreaks from all produce
commodities, including leafy greens, vine crops, and fruits and nuts are combined, produce
accounts for more outbreaks in the United States than any other food type (CDC 2011). Between
1973 and 1997, the number of outbreaks which were linked to consumption of contaminated
produce increased 8 fold, while the total number of foodborne illness outbreaks from all food
souce remained approximately the same (Sivapalasingam et al. 2004).
FIGURE 2.3. Causes of illness in 1565 single food commodity outbreak, 2003-2008. (CDC
2011).
There are many reasons why the number of outbreaks traced to contaminated produce is
increasing. First, there have been dramatic changes within the produce industry including
centralized production and more distant distribution channels which lead to longer times from
24 harvest to consumption and more consumers receiving product from the same farm (Tauxe et al.
1997; Sivapalasingam et al. 2004; Franz and van Bruggen 2008; Hanning et al. 2009; Alsanius
2010; Berger et al. 2010; Deering et al. 2011). New produce products which employ
technologies for cutting, slicing, and shredding, which remove the natural protective barriers
against surface bacteria growth are becoming more popular (Franz and van Bruggen 2008).
Increased urbanization of rural areas has also led to the development of produce farms closer to
other types of agricultural operations (cattle ranches and chicken farms) and housing
developments, both of which can be reservoirs for harmful bacteria that may increase the
potential for contamination of produce farms (Deering et al. 2011). Globalization of the food
industry has led to increases in the amount of produce which is imported (13% of vegetables,
32% fruits in 2007) (Sivapalasingam et al. 2004; Buzby et al. 2008). Many international farms
are not subjected to the same food safety standards as domestic farms; however, only 1% of
produce that is imported is inspected (Sivapalasingam et al. 2004; Buzby et al. 2008).
Additionally, changes in consumer trends may have contributed to the rising number of
produce-related outbreaks. Produce consumption has increased more than 24% over 20 years
(Sivapalasingham et al. 2004). This increase can be attributed to changing diet trends, increased
awareness of public health, and programs from the United States government that seek to reduce
cancer and cardiovascular disease by increasing consumption of fruits and vegetables (Tauxe et
al. 1997; Franz and van Bruggen 2008; Hanning et al. 2009; Alsanius 2010; Berger et al. 2010;
Deering et al. 2011). Produce poses increased food safety risks over other commodities because
it is often consumed without cooking and it is often not prepackaged, thus leaving it susceptible
to contamination (Hanning et al. 2009). In addition, there are an increasing number of
individuals at high risk of illness from pathogens, including the elderly, transplant patients and
25 other immunocompromised patients (OMAFRA 2010). Finally, epidemiological surveillance of
foodborne illness has improved with improved diagnostics, which has led to better identification
of outbreaks and greater ability to trace outbreaks to the source (Alsanius 2010; Berger et al.
2010).
The number of cases per outbreak is higher for produce than any of the other five major
commodity groups (produce, beef, eggs, poultry, seafood) (Figure 2.4). Because of the high
number of outbreaks and high average number of cases per outbreak, produce accounts for the
highest proportion of foodborne disease cases, 38% of all foodborne illnesses (CSPI 2008).
FIGURE 2.4. Average number of illnesses per outbreak in single food commodity outbreaks,
1998-2005 (CSPI 2008).
Cases per outbreak in produce-related outbreaks are high because traceability, the ability
to track a produce from the consumer back to the farm on which it was produced, is very difficult
to achieve (Doyle and Erickson 2008). When traceback is complicated and takes a long period of
time, produce cannot be recalled and consumers cannot be informed of the hazard, which can
lead to a greater number of cases. Consumers often have limited recall about the foods they have
eaten and the short shelf life of produce can make it difficult to identify common food products
consumed during an outbreak (Hanning et al. 2009; Danyluk and Schaffner 2011). With so many
26 new types of produce and produce mixtures available to consumers, some consumers are often
unaware of exactly what produce they have eaten (Berger et al. 2010). Consumers also do not
notice, and therefore do not recall, produce which was used as a minor part of a meal, for
example as a sandwich topping or as a garnish, which, despite the small amount, can still pose a
health threat (Berger et al. 2010). Trace back from point of sale to the farmer is also hindered by
the fact that it requires a brand name, date of purchase, UPC code, and lot number, information
which is not always available for produce (Hanning et al. 2009). Finally, produce fields are
turned over very quickly after harvest, and there often is no product left at the farm level to be
tested or traced back (Dolye and Erickson 2007; Danyluk and Schaffner 2011).
Produce can become contaminated during any stage of production – cultivation,
harvesting, handling, storage, or processing (Tournas 2005). Healthy, asymptomatic agricultural
animals, primarily cattle, are considered the natural reservoir for E. coli O157:H7 and
Salmonella, so pathogen contamination is often transmitted to produce via fecal contamination
(Fernandez-Alvarez et al. 1991; Franz and van Bruggen 2008; Danyluk and Schaffner 2011).
Sources of contamination can include irrigation water, application of animal waste, raw sewage
waste, soil, wild or domesticated animals, or unclean hands or equipment. (Doyle and Erickson
2008; Hanning et al. 2009).
2.4.2. Produce related foodborne illness outbreaks
Sixty percent of foodborne illness outbreaks that could be traced to a specific produce source
were caused by bacterial pathogens, 20% by viruses, 16% by parasites, and 4% by toxic
chemicals (Sivapalasingam et al. 2004). Salmonella spp. and E.coli O157:H7, are the first and
second most common bacterial pathogens implicated among causes of produce related foodborne
27 illness outbreaks (Table 2.1) (Franz and van Bruggen 2008; Alsanius 2010). Salmonella spp. are
routinely isolated from produce in surveys; however, the level of contamination varies greatly
between regions and farm types (Thunberg et al. 2002). A European study reported Salmonella
in 0.3% of produce samples (Westrell et al. 2009), whereas a study of cantaloupe and chili
pepper farms found Salmonella in 43% of produce in the field (Teplitski et al. 2009). In addition,
E. coli O157:H7 is an important produce contaminant, as produce alone accounted for 21% of all
E. coli O157:H7 outbreaks between 1982 and 2002, and 34% of all E. coli O157:H7 cases
(Rangel et al. 2005).
TABLE 2.1. Causes of produce-related foodborne outbreaks in the United States, 1973 to 1997
(Data from Sivapalasingham et al. 2004).
Organism
Salmonella
E. coli O157:H7
Hepatitis A
Shigella
Norovirus
Cyclespora cayetanensis
Giardia lamblia
Chemical or Toxin
Campylobacter
Cryptosporidium parvum
Non-O157 E. coli
Bacillus cereus
Yersinia enterocolitica
Staphylococcus aureus
Etiological Agent not Identified
Number of Produce Related Outbreaks
30
13
12
10
9
8
5
4
4
3
2
1
1
1
87
Escherichia coli O157:H7 has been linked to outbreaks in apples, watercress, leafy
vegetables, lettuce, parsley, radishes, spinach, tomatoes, and sprouts (Alsanius 2010; Berger et
al. 2010). Between 1998 and 2005 there were 20 outbreaks of E. coli O157:H7 in lettuce alone,
leading to 634 illnesses (Brandl and Amundson 2008). As previously mentioned, outbreaks in
28 produce have above average numbers of cases per outbreak, which helps explain why the largest
outbreak of E. coli O157:H7 was traced to consumption of contaminated radish sprouts in Japan
in 1996 (Michino et al.1999). It was once thought that low pH products would prevent problems
with human pathogens; however, both Salmonella and E. coli O157:H7 have been linked to
consumption of acidic fruit juices and cider (Sivapalasingam et al. 2004; Berger et al. 2010).
Salmonella spp. have been linked to outbreaks in tomatoes, Serrano peppers, cantaloupe,
lettuce, basil, mango, sprouts, melon, cauliflower, spinach, mushrooms, and jalapeno peppers
(Sivapalasingam et al. 2004; Takkinen et al. 2005; Pezzoli et al. 2008; Alsanius 2010; Berger et
al. 2010; Levantesi et al. 2011). In 1995 an outbreak of Salmonella Stanley occurred
simultaneously in the United States and Finland (Mahon et al. 1997). This outbreak was
eventually traced to sprouted seeds from a Dutch seed supplier, and it helped illustrate the
potential food safety problems associated with a globalized food system. Produce commodities
which were implicated in foodborne illness outbreaks between 1998 and 2006 are shown in
Figure 2.5. Salads and lettuce are the most often implicated products.
FIGURE 2.5. Commodities implicated in produce related foodborne illness outbreaks in the
United States, 1998-2006 (CSPI 2008).
29 2.5. GOOD AGRICULTURAL PRACTICES
In response to increases in produce outbreaks, government agencies decided there was a
need to adopt risk management strategies to reduce the likelihood of crop contamination (Berger
et al. 2010). On-farm prevention practices and risk management strategies are often referred to as
Good Agricultural Practices (GAPs). Good Agricultural Practices can be categorized into 8 areas
of concern: (1) worker hygiene, (2) livestock and wildlife control, (3) facilities and equipment
control, (4) water quality, (5) application of animal manure, fertilizer and soil amendments, (6)
post-harvest storage, (7) transportation, and (8) traceability (USDA and FDA/CFSAN 1998).
In 1998, the United States Department of Agriculture (USDA) and the United States
Food and Drug Administration (FDA) created a guidance document for produce growers titled
the “Guide to Minimize Microbial Food Safety Hazards for Fresh Fruit and Vegetables” (USDA
and FDA/CFSAN 1998). This document identifies preventative measures on produce farms to
with the goal of increasing produce safety (USDA and FDA/CFSAN 1998). The FDA has
modified these recommendations for some high risk industries - tomatoes, melons, leafy greens,
sprouted seeds, and fresh cut produce (FDA 2009a, FDA 2009b, FDA2009c, FDA 1999). The
guidelines in these documents are currently voluntary and not federally mandated; however, the
USDA offers a voluntary third party, fee-based audit. Based on these guidelines, some industries,
including leafy greens, tomatoes, melons, mushrooms, green onions, and berries, have chosen to
adopt these guidelines or create their own set of guiding principles to increase the safety of their
products (Sivapalasingam et al. 2004). The 2006 outbreak of E. coli O157:H7 in spinach
accelerated efforts by the leafy green industry to “define meaningful and measureable prevention
practices and standardized audit criteria.” (LGMA 2012).
30 2.6. IRRIGATION WATER AS A SOURCE OF PRODUCE CONTAMINATION
2.6.1. Irrigation water as a vehicle for contamination
Although contamination of produce crops can occur anywhere throughout the agricultural
production system, irrigation water and manure are considered the most common sources (Franz
and van Bruggen 2008). The use of manure may seem to be the most likely source of fecal
pathogen contamination; however, in the US, recent focus has been on irrigation water. This may
be because most conventional farms use synthetic chemical fertilizers rather than animal manure,
and because the majority of the high risk crops grown in the United States are produced in semiarid regions where irrigation is necessary (Franz and van Bruggen 2008). Fresh produce can be
irrigated by groundwater, surface water, reclaimed or recycled water, or a mixture of these
sources (USDA NASS 2009). In the United States 70% of commercial farmland is irrigated,
which accounts for approximately 7 million acres of fruit and vegetable farms (USDA NASS
2009). Between 2003 and 2008 there was a 12% increase in the use of groundwater for irrigation
and 22% increase in the use of surface water (NASS 2009).
Irrigation water has been shown to contain human pathogens including Salmonella spp.
and E. coli O157:H7 (Steele and Odumeru 2004; Materos et al. 2006). Each time that irrigation
water contacts the edible part of the crop there is the potential for contaminants to be transferred
from the water to the crop (OMAFRA 2010; Bihn and Reiners 2011). Based on data for
pathogen outbreaks associated with produce products, irrigating crops with contaminated water
or untreated wastewater increases the frequency of cases of illness in children (Ait Melloul and
Hassani 1999; Chambers et al. 2002).
Crop contamination can occur through uptake of water via the root system, directly
deposited on above ground plant surfaces via overhead irrigation, or indirectly by splashing from
31 the soil (Franz and van Bruggen 2008). Some studies have shown that once Salmonella spp. and
E. coli O157:H7 have contaminated a crop by overhead irrigation, they can become internalized
into tomatoes, radish sprouts, bean sprouts, barley and lettuce (Itoh et al. 1998; Guo et al. 2002;
Solomon et al. 2002a; Warriner et al. 2003; Kutter et al. 2006; Klerks et al. 2007; Schikora et al.
2008; Lapidot and Yaron 2009; Erickson et al. 2010; Deering et al. 2011;). Internalization can
occur through natural openings (pores, stomates, lenticles) or after physical damage (cuts and
bruises) to the crop. They can also be pulled into the crop through the root system if water which
is taken up by the crop is contaminated (Deering et al. 2011). Studies have shown that once
pathogens are internalized into the crop they are resistant to sanitizer treatments normally used in
produce production (Solomon et al. 2002a; Solomon et al. 2002b).
2.6.2. Risk factors affecting the safety of irrigation water
There are 4 main ways that the risk of contamination from irrigation water can be
mediated by growers: the method of application, the timing of application, the surrounding
environment, and the selection of the type of water used.
Method of Application
Irrigation water can be applied to crops using overhead, furrow, flood, seep ditches,
surface drip, or subsurface drip irrigation methods (Bihn and Reiners 2011). Methods which do
not allow the water to contact the edible portion of the crop, such as drip methods, are less likely
to transfer contaminants to the crops than other methods (OMAFRA 2010; Suslow 2010). In the
United States over half of all the irrigated farm land was irrigated by overhead irrigation, the
highest risk method (NASS 2009). A study of lettuce irrigated with water contaminated with E.
32 coli O157:H7 showed that the overhead irrigation resulted in more contamination (91%)
compared to lettuce irrigated by drip irrigation (19%) (Solomon et al. 2002a). Increasing the use
of drip irrigation methods may therefore be a way for growers to lower their risk of pathogen
transmission from contaminated waters.
Timing of Application
It is believed that food safety risks are higher when irrigation water is applied closer to
harvest. This is because pathogens survive over limited time ranges; however, there are no
consistent time intervals established for how close to harvest irrigation water can be applied. In
fact, there is some research that indicates some pathogens can survive on plant surfaces
throughout the entire growing season. (Ercolani 1979; Solomon et al. 2002a; Islam et al. 2004a;
Islam et al. 2004b).
Studies have shown that when seedlings are exposed to either overhead or drip irrigation
water which has been contaminated with E. coli O157:H7 or Salmonella spp., these pathogens
can persist on the plant until after the plant would be harvested, up to 6 months (Ercolani 1979;
Solomon et al. 2002a; Islam et al. 2004a; Islam et al. 2004b). Salmonella spp. and E. coli
O157:H7 have been shown not only to just survive on the surface of plants, but to actually
reproduce to high levels (Beuchat 2002; Warriner et al. 2003; Cooley et al. 2003; Jabasone et al.
2005; Schikora et al. 2008; Deering et al. 2011). Once Salmonella spp. and E. coli enter the soil,
through the application of overhead irrigation with contaminated water, they can persist in the
soil for up to 231 days and 196 days respectively, regardless of what type of crop is grown
(Islam et al. 2004a; Islam et al. 2004b, Islam et al. 2005; Johannessen et al. 2005). One study
showed that at optimal temperatures, Salmonella spp. can survive in soil for several years (Reddy
33 et al. 1981). Studies have examined the persistence of pathogens in crops using different
combinations of bacteria, crops, and contamination methods; however, even in studies that
examine the same combinations there are discrepancies in the length of pathogen survival
(Deering et al. 2011). These results suggest that microbial persistence in crops is based on many
factors including the crop, the strain and serovar of bacteria, the route of contamination, plant
age, and contamination of soil (Deering et al. 2011), all of which complicate the creation of riskbased food safety strategies.
Surrounding Environment
Another way to mediate risk is to control the surrounding environment and the potential
for contaminants to enter the water system. Growers should be aware of all potential
contamination sources (manure piles, feedlots, septic systems, chemical storage, pet or animal
enclosures, etc) and not allow these contaminants in close proximity to the water source
(OMAFRA 2010). In addition growers can limit wild and domestic animal access to the water
source through activities including noise cannons and fencing (Bihn and Reiners 2011). Growers
should also be aware of the elevation of their water source and try to find a source which is not
located in a flood zone, and which is located at a higher elevation than possible contaminants.
Source of Water
Irrigation water can be drawn from a number of sources including, wells, ponds, rivers,
streams, and municipal water sources. These water sources can be divided into 3 groups,
municipal and other treated water sources, groundwater, and surface water. Municipal water has
the lowest risk of contamination because it is usually treated with a sanitizer and its quality is
34 frequently monitored by trained personnel. Groundwater, which is mainly accessed through
wells, is more susceptible to contamination because wells are often maintained by untrained
personnel and are not treated as frequently as municipal water. Well water is often not tested for
microorganisms which would lower the risk of using contaminated water. Surface water includes
all water sources open to contamination from wild or domesticated animals, agricultural or storm
runoff, or faulty septic systems and therefore poses the highest risk of microbial contamination
(Steele and Odumeru 2004; OMAFRA 2010; Bihn and Reiners 2011). Surface water is also the
most variable type of water when it comes to microbial levels because it can become temporarily
contaminated due to sudden upstream contamination, heavy rainfall, run-off, flooding, or animal
activity (Steele and Odumeru 2004; OMAFRA 2010; Bihn and Reiners 2011).
2.7. UNIQUE RISKS ASSOCIATED WITH THE USE OF SURFACE WATER FOR
IRRIGATION
2.7.1. Surface water as a vehicle for contamination
Human pathogens are routinely recovered from surface waters which are used for
irrigating (Gallegos-Robles et al. 2008). The use of surface water has been shown to contribute
to produce contamination in the field (Islam et al. 2004a; Ibenyassine et al. 2005; Rai and
Tripathi 2007; Gallegos-Robles et al. 2008). The risk of finding pathogens in water varies by
region, locality, and season (Suslow 2010). The highest risk time for finding pathogens in
surface water occurs during the hot summer months; however, once introduced into the source,
pathogens die off faster in these warm summer months when non-pathogenic background
bacteria in the water provide competitive inhibition (Rhodes and Kator 1988). One of the unique
risk factors of surface water is the interaction between the water system and sediments, banks,
35 sands, algae, and plants. Pathogens have been shown to survive for longer times when attached
to sediment and algae compared to bulk water (Suslow 2010). Mixing of algae or sediment into
the bulk water, by means of waves, storms, dredging, and removal of aquatic plants, can release
pathogens into the bulk water and these factors need to be taken into account when assessing the
risk of an irrigation water source (Suslow 2010).
2.7.2. Foodborne Illness Outbreaks Linked to Contaminated Surface Water
Produce outbreaks are difficult to trace back to the product source and even harder to
trace to the original source of contamination, for reasons previously discussed (section 2.4.1);
however, there have been a number of foodborne illness outbreaks where strong circumstantial
evidence pointed to contamination that originated from surface water used for irrigating produce.
In 1995, 40 Montana residents were sickened by E. coli O157:H7 after consuming lettuce
which had been irrigated with water from a pond that was fed by several streams (Ackers et al.
1998). Although the outbreak strain was never recovered from the pond or streams, several of the
feeder streams passed through fields where cattle had grazed (Ackers et al. 1998). In 1996, 49
people in 2 states were diagnosed with E. coli O157:H7 linked to mesclun lettuce (Hilborn et al.
1999). Similar to the Montana outbreak, a matching strain was not isolated from the irrigation
water source, but was found in cattle which were grazing in a nearby field (Hilborn et al. 1999).
In 2005 an outbreak of 135 cases of E. coli O157:H7 was traced to a farm were a small stream
was used to irrigate lettuce, and the outbreak strain was isolated from cattle grazing upstream
(Soderstrom et al. 2008). In 2006 an outbreak of E. coli O157:H7 sickened 80 people who ate at
Taco Johns restaurants in two states (FDA and CFERT 2008). Shredded lettuce used at these
restaurants was contaminated after well water intended for use in irrigation was accidentally
36 mixed with water from a dairy lagoon where the outbreak strain of E. coli O157:H7 was isolated
(FDA and CFERT 2008). In 2006, an outbreak of E. coli O157:H7 from fresh, pre-packaged
spinach grown in California caused more than 200 illnesses across 26 states (Gelting 2007). This
outbreak resulted in an in-depth investigation into the source of the contamination. Although no
definitive source was ever identified, the use of surface water that was open to both agricultural
runoff and accessible to upstream cattle was identified as a potential source (Gelting 2007). This
widely publicized outbreak cost the U.S. spinach industry over $75 million dollars and damaged
the reputation of the entire leafy green industry for an extended period of time (FDA 2008a).
Surface water used for irrigating tomatoes has been linked to a recurrent multistate
outbreak (Greene et al. 2008). In 2002, 510 people across 26 states were sickened by a rare strain
of Salmonella Newport after eating tomatoes grown in Virginia. In 2005, 72 people from 16
states were diagnosed with an identical strain of Salmonella Newport after consuming Virginia
tomatoes. After this second group of illnesses occurred, the implicated strain was isolated from
pond water which had been used for irrigation (Greene et al. 2008).
In 2008, an outbreak of Salmonella serovar Saintpaul swept across the country sickening
1,442 people in 43 states, the District of Columbia, and Canada (CDC 2008). Originally, the
CDC implicated tomatoes as the source of the outbreak; however, this initial conclusion turned
out to be incorrect as serrano and jalapeno peppers were eventually determined to be the
outbreak vehicle. The outbreak strain was isolated from a holding pond used for irrigating
jalapeno and Serrano peppers on the Mexican farm where the contamination originated.
Although eventually tomatoes were found not to be the cause, this outbreak caused the tomato
industry enormous economic hardship. It is estimated that in Florida alone the industry lost over
100 million dollars (Produce Safety Project 2010).
37 2.7.3. Microbial Standards for Irrigation Water
Currently, there is no nationally enforced standard for irrigation water. This is because
there is a lack of consensus on which type of indicator organism should be used, maximum
levels allowed, the frequency of surface water sampling, and the monitoring area (Griffin et al.
2001). It is expected that the Food Safety Modernization Act (FSMA) [P.L. 111-353] signed into
law into 2011 will be used to create federal standards for on-farm food safety practices, including
microbial limits for irrigation water. Despite the absence of a national standard, some industry
groups and private auditing companies have developed standards for their own use. Lists of
voluntary government and industry standards are shown in Tables 2.2 and 2.3 respectively.
FDA guidance documents do not set a microbial limit for the use of surface water used
for irrigation, but instead place the burden onto producers to ensure that water is “of appropriate
quality for its intended use, obtaining water from an appropriate source, [and that they undergo]
treating and testing water on a regular basis and as needed to ensure appropriate quality” (FDA
2009a; FDA 2009b; FDA 2009c; FDA 2008b; FDA 1999). In contrast, the industry guidelines
set very specific standards based on maximum allowable levels of indicator organisms.
Discrepancies between types and amounts of indicators selected for standards reflects
uncertainty about the association of pathogens and indicator organisms and the potential for
transmission of pathogens from surface water to crops (Steele et al. 2005). The lack of a single,
science-based standard that correlates well with pathogen presence is one of the key barriers to
developing common surface water testing requirements (Suslow 2010). Without a mandated
standard and surface water testing program however, producers find it difficult to make informed
decisions regarding which commodities can safely be irrigated at what time, what source to use
for irrigation, and whether to sacrifice yield by choosing not to irrigate with high risk water.
38 TABLE 2.2. List of current government standards for the use of recreational or surface water
Guidance Document
Target Microorganism
Standard
Reference
US EPA Bacterial Water Quality Standards for
Recreational Waters – National Standard
Generic E. coli
126 CFU/100mL
EPA 2003
Enterococci
33 CFU/100mL
PA DEP Bacterial Water Quality Standards for
Recreational Waters – Pennsylvania Standards
US FDA Guide to Minimize Microbial Food
Safety Hazards of Tomatoes
US FDA Guide to Minimize Microbial Food
Safety Hazards of Leafy Greens
US FDA Guide to Minimize Microbial Food
Safety Hazards for Sprouted Seeds
US FDA Guide to Minimize Microbial Food
Safety Hazards of Melons
US FDA Guide to Minimize Microbial Food
Safety Hazards for Fresh-Cut Fruit and
Vegetables
USDA AMS Fresh Produce Audit Verification
USDA National Organic Program Production
and Handling Requirements
CCME Water Quality Guidelines for the
Protection of Agriculture
WHO Health Guidelines for the Use of
Wastewater in Agriculture and Aquaculture.
Fecal Coliform
EPA 2003
None
200 CFU/100mL during swimming
5000 CFU/100mL at other times
None
None
None
FDA 2009b
Salmonella and E. coli O157:H7
0 CFU/100mL
FDA 1999
None
None
FDA 2009c
None
None
FDA 2008b
None
None
None
None
Coliforms and
E. coli
Fecal Coliforms
1000 CFU Coliforms/100mL
100 CFU E. coli/100mL
1000 CFU/100mL
USDA 1998
USDA NOP
2010
CCME 1987
FDA 2009a
WHO 1989
39 TABLE 2.3. List of current industry and commodity group standards for the use of recreational or surface water
Guidance Document
Primus Labs Greenhouse Audit
Requirements
Primus Labs Ranch Audit Guidelines
GlobalGAP
Commodity Specific Food Safety Guidelines
for the Production and Harvest of Lettuce
and Leafy Greens. (Leafy Greens Marketing
Agreement)
Target
Microorganism
Generic E. coli
Salmonella and
E. coli O157:H7
Generic E. coli
Salmonella,
E. coli O157:H7
Fecal Coliform
Generic E. coli
Mushroom Good Agricultural Practices
Program. (American Mushroom Institute and
Penn State)
Commodity Specific Food Safety Guidelines
for the Production, Pre-Harvest, Harvest, and
Value Added Unit Operations of Green
Onions
Commodity Specific Food Safety Guidelines
for the Fresh Tomato Supply Chain. (North
American Tomato Trade Working Group)
None
California Strawberry Commission Food
Safety Program (California Strawberry
Commission)
None
Generic E. coli
Total Coliforms
and E. coli
Standard
≤126 MPN/100mL (rolling
geometric mean n=5)
≤235 MPN/100mL for any single
Sample
0 CFU/100mL
≤126 MPN/100mL (rolling
geometric mean n=5) ≤235 MPN/100mL for
any single sample
0 CFU/100mL
1000 CFU/100mL
≤ 126 MPN/100mL (rolling
geometric mean, n=5)
≤235 MPN/100mL (any one sample
for foliar contact water)
≤576 MPN/100mL (any one sample
for non-foliar contact water)
Irrigation water shall meet EPA microbial standards
for drinking water
≤126 MPN/100mL (rolling
geometric mean n=5)
≤235 MPN/100mL (for any single
sample)
0 CFU Coliforms (for foliar contact
water)
≤126 MPN/100mL E. coli
None
Reference
Primus Labs 2007b
Primus Labs 2007a
GlobalGAP 2010
LGMA 2012
AMI and PSU 2008
WGA 2010
North American
Tomato Trade
Working Group
2008.
California
Strawberry
Commission 2005
40 Many of the industry standards were based on the United States Environmental
Protection Agency’s (EPA) Recreational Water Standards (LGMA 2012). EPA recreational
water standards were mandated based on maximum levels of E. coli and enterococci previously
shown to reduce gastroenteritis illness among bathers to less than eight per 1000 (EPA 1984;
EPA 2003). Because recreational water standards were based on the risk involved with human
contact with surface water, they do not take into account the factors of plant production which
may alter the food safety risk such as individual crop characteristics and environmental
conditions that influence survival of the pathogen on the fruit or vegetable. In retrospect, the
choice to apply these EPA standards to irrigation water does not seem like a sound scientific
decision; however, it did provide a place to begin surface irrigation water testing and it helped
restore customer confidence (Suslow 2010).
A number of the industry standards which are based on the EPA recreational water
standards employ a rolling geometric mean, for example the Leafy Greens Marketing
Agreement. A rolling geometric mean takes the average of a certain number of water tests, in this
case the previous five water tests and compares this mean to a standard. The use of a mean helps
to reduce the effect of one outlying test result. In these standards, a maximum level for any
single sample is created in additional to a maximum level for the rolling geometric mean.
Some surface water standards are not based on EPA standards. The outbreak of
Salmonella enterica servoar Saintpaul in 2008, which had a strong economic impact on the
tomato industry, caused the industry to implement industry microbial standards, which included
adopting the World Health Organization standard of less than 1000 CFU/100mL of coliform for
surface water used for irrigation (United Produce Association 2009). However, this international
standard for irrigation water has since been deemed inappropriate for use by United States public
41 health agencies (Suslow 2010), and the standard has since been changed to 1000 CFU/100mL of
fecal coliform (North American Tomato Trade Working Group 2008).
A number of studies have investigated the correlation between pathogens and indicator
organisms. Some studies found higher levels of specific indicators in samples where pathogens
were found (Arvanitidou et al. 1995a; Arvanitidou et al. 1995b; Polo et al. 1998; Ijabadeniyi et
al. 2011); however, in these studies inconsistent correlations were found. A number of other
studies have found no significant correlation between fecal indicator bacteria and pathogens,
including Salmonella spp., E. coli O157:H7, Campylobacter spp., Yersinia spp., Listeria
monocytogenes, Staphylococcus aureus, viruses, and protozoa (Carter et al. 1987; Arvanitidou et
al. 1995a; Lemarchand and Lebaron 2003; Horman et al. 2003; Harwood et al. 2004; Shelton et
al. 2011; Ijabadeniyi et al. 2011).
Generic E. coli has been deemed by many as the indicator organism of choice, but a
suitable standard which reliably differentiates safe and unsafe surface water sources is needed
(Suslow 2010). Among east coast states, Virginia has performed one of the most comprehensive
assessments of the microbial quality of rivers and streams (Shelton et al. 2011). As of 2008,
15,873 miles of rivers and streams had been assessed, and 38% (5989 miles) of those waters
were found to have E. coli levels which exceeded any of the current standards for this organism.
This assessment shows that adoption of any of the current E. coli standards could have adverse
economic impacts on produce growers who rely on surface waters for irrigation (Shelton et al.
2011). In this study which compared levels of generic and pathogenic E. coli, the authors
concluded based on the high levels of generic E. coli they found that “E. coli data might not be
suitable to predict the risk of exposure to other pathogenic strains. Standards based on E. coli
concentrations might be both unduly stringent and simultaneously inadequate to provide a
42 reliable margin of safety. Consequently, it might be desirable to establish alternative criteria for
surface waters” (Shelton et al. 2011). Before accepting E. coli as the indicator organisms to be
used in all surface water testing, more work needs to be done to determine if any E. coli-based
standard accurately predicts the safety of a water source (Suslow 2010).
Often studies have demonstrated that current surface water standards can be both too
strict and too lenient (Polo et al. 1998; Haley et al. 2008). Haley et al. (2008) reported that
Salmonella was present in 100% and 84% of samples when USEPA recreational water standards
for E. coli and enterococci respectively were exceeded; however, Salmonella was also found in
76% and 71% of samples where levels of E. coli and enterococci respectively were below
USEPA limits. In this case the current standards were not sufficiently strict to identify all water
samples which contained Salmonella spp. In contrast, Polo et al. (1998) found that many samples
which failed limits set by European directives for total coliforms (10,000 CFU/100mL) and fecal
coliforms (2,000 CFU/mL) were Salmonella spp. negative.
Some microbial surveys of surface waters have reported correlations between indicator
organisms levels and physical and environmental characteristics. In several studies, microbial
levels were positively correlated to precipitation levels or recent extreme rain events (Ferguson
et al. 1996; Steele et al. 2005; Haley et al. 2008). Ferguson found that indicator organism levels
were significantly higher in sediment than in the associated bulk water, thus explaining positive
correlation with the turbidity of a water sample. It has also been shown that many types of
common algae can support the growth of microbial indicator organisms, so higher levels of algae
could contribute to the microbial levels of a water source (Byappanahalli et al. 2003a; Suslow
2010).
43 A uniform national standard applicable to all regions, climates, crops, production scales,
water types, and application methods would be unattainable without allowing unacceptable risk
to consumers or cause undue hardship on producers (Suslow 2010). Science should be used to
create a flexible, risk-based standard which accounts for some of these differences between water
sources and crops (Suslow 2010). In addition to one unified standard, water sampling protocols,
which include sampling frequency, timing of sampling relative to irrigation, and the size and
method of sample collection, need to be standardized. There are many factors which affect the
risk to consumers. For instance, products which are consumed without washing or processing are
of higher risk for transmitting pathogens than products which are cooked (OMAFRA 2010). In
addition, products with large surface areas which can trap and hold moisture are more
susceptible to contamination by irrigation water than other crops (Steele et al. 2005). These
differences in crop risk need to be taken into account when developing a standard. Some of the
standards which have been developed make use of a rolling geometric mean for comparison to a
standard instead of individual samples (LGMA 2012). This approach may lessen the hardship on
growers as it decreases the risk of a single abnormal water test causing a failure
In order for an indicator organism to be an effective measure of the safety of a water
source it should consistently and specifically correlate with the health risks associated with fecal
contamination; however, the current fecal indicator organisms have not been shown to meet this
requirement (Caplenas and Kanarek 1984; Jiang et al. 2001; Noble and Fuhrman 2001;
Bonadonna et al. 2002; Lemarchand and Lebaron 2003; Suslow 2010;). Fecal indicator
organisms have been shown to be ubiquitous in the environment (Fujioka et al. 1999; Whitman
et al. 2003; Wheeler et al. 2003; Byappanahalli et al. 2003b; Power et al. 2005) and their
presence in a water source is not necessarily indicative of survival, persistence, or presence of
44 pathogens, both bacteria and viruses (Cheong 2009; Suslow 2010). Fecal coliforms and
enterococci have both been found in non-agricultural soils and waters far from human activity,
suggesting their presence does not indicate recent human or domestic animal fecal contamination
and pathogen presence (Griffin et al. 2001).
The presence or survival of indicator organisms in the absence of pathogens and the lack
of correlation to human pathogens therefore suggests that the use of indicator organisms for
assessing microbial safety of a water source may make the process unnecessarily self-penalizing
(Suslow 2010). The effect of physical and environmental characteristics on levels of microbial
indicator organisms also raises questions about their ability to accurately predict the safety of a
water source.
2.8. PROTOCOLS FOR WATER SAMPLING AND WATER SAMPLE HANDLING
Standard methods for collecting water samples dictate that a single sample collected at a
specific location and time shall be collected in sterile bottles of adequate volume (APHA 2005).
More detailed methods on optimal frequency of sampling and from what depth the sample
should be taken do not exist; however methods for sampling handling and storage have been
established. For instance, The American Public Health Association mandates the maximum
amount of time a water sample can be held between sample collection and analysis (APHA
2005). Specific time intervals depend on the type and the end use of the water source. Samples
from water sources of low microbial contamination to be used for drinking can be held for 30
hours prior to analysis for coliforms and 8 hours for total plate count (APHA 2005). Nonpotable
water to be analyzed for microbial compliance (including surface water to be used for irrigation)
must be delivered to the laboratory within 6 hours of sampling, and analyzed at the laboratory in
45 under 2 hours (APHA 2005). Throughout the transport interval, the sample must be held below
10°C (APHA 2005).
This six hour time limit has been stated in all editions of the Standard Method for the
Examination of Water and Wastewater as far back as 1928 (APHA 1928), although it is unclear
on what scientific evidence this was based (McCarthy 1957; Standridge and Lesar 1977).
Interestingly, Standridge and Lesar (1977) reported evidence that this limit was based on the
time required to travel by horse and buggy from the British Minister of Health’s laboratories to
the Thames River and back again.
For small operations that do not have the capability to test for E. coli onsite and are not
within driving distance of an accredited microbial laboratory, this 6 hour time limit is difficult to
achieve (Pope et al. 2003). It has been proposed that this time limit could be extended if
microbial levels do not change beyond generally accepted confidence intervals (Selvakumar et
al. 2004); however, a review of the literature shows that a scientific consensus has not been
reached on the implications of increasing the holding time.
Some studies have shown no significant effect of post sampling holding times on levels
of target microorganisms (Standridge and Lesar 1977; Aulenbach 2010). Standridge and Lesar
(1977) and Aulenbach (2010) reported in two separate studies that there was little change in
coliform levels in samples with high initial levels held between 2 and 4°C for 24 hours.
Standridge based his analysis of acceptable variation on an EPA regulation which requires an
80% agreement between proposed new microbial methods (Geldreich 1975). In his study, he
reported that coliform levels in 24 of 28 samples differed by less than 20% (Standridge and Lesar
1977). In addition, Aulenbach (2010) showed that fecal coliform levels did not change
significantly (p ≤ 0.05) when held up to 62 hours at refrigeration temperatures. Pope et al. (2003)
46 studied changes in E. coli levels as holding time was increased and found that, only 25% showed
significantly different levels (p ≤ 0.05) when sample temperatures were maintained at 10°C or
lower (Pope et al. 2003). When examining the effect of holding time on coliform and fecal
coliform levels, Dutka and el-Shaarawi (1980) showed that only 25% of samples showed a
significant difference (p ≤ 0.05) in microbial levels after holding for 24 hours.
Conversely, a number of other studies found that increasing holding times did have a
significant effect on microbial counts. Studies on drinking water showed that after 24 and 30
hours at ambient temperature, coliform reductions were 23% and 33%, respectively (Jones et al.
1950; Mcdaniels et al. 1985). Caldwell and Parr (1935) reported even higher losses of coliforms,
up to 40-50%, in samples stored on ice. Selvakumar et al.(2004) studied a variety of indicator
organisms, including total coliforms, fecal coliforms, E. coli, and enterocci, and showed that
only coliforms and fecal coliform levels remained stable after 24 hours at 4°C (Selvakumar et al.
2004).
The temperature at which a sample was held may impair how sample holding will affect
microbial levels. Pope et al. (2003), found no significant difference in levels of E. coli when
samples were held at 4°C and significant variation in only 25% of samples when held at 10°C;
however, in those samples held at 20°C or 35°C, significant differences were seen in E. coli
levels in 75% of samples.
Initial bacterial levels may also alter the effect of increased holding times on surface
water. A trend found in many of these studies is that an increase in holding time was less
influential on those samples with lower initial levels of bacteria (McCarthy 1957; Standridge and
Lesar 1977; Selvakumar et al. 2004).
47 These studies show that further work needs to be done to assess the effect of holding time
and temperature on microbial levels in water samples. Until such time, it may prudent to
refrigerate samples immediately after collection and analyze them as quickly as possible.
48 CHAPTER 3. SURVEY OF SURFACE WATER SOURCES USED FOR IRRIGATION
OF SPECIALTY CROPS IN PENNSYLVANIA
3.1 ABSTRACT
Foodborne illness outbreaks linked to contaminated irrigation water from surface sources
have created a need to establish microbial standards for agricultural water. Surface water is
considered the least microbiologically safe type of water due to the fact that it is open to the
environment. Although there are no national legally enforced standards for microbial safety of
irrigation water for growing produce crops, several government and commodity groups have
developed their own surface water standards. The purpose of this study was to survey microbial
indicators and select human pathogens in surface waters used for irrigation of fresh produce
crops in Pennsylvania, compare these levels to current surface water standards, and determine if
indicator microorganisms reliably predict the presence of pathogens. Over the course of two
growing seasons, 153 water samples were collected from 39 farms at three times during the
growing season. Data were collected for six physical attributes, fourteen environmental
characteristics, five indicator organisms, and two pathogens. Of all the physical characteristics
tested, only pH showed a significant correlation to all indicator organism levels. Results at
individual farm locations were highly variable between sampling times. Approximately 50% of
all samples collected failed each of the current surface water standards; however, indicator
organism standards did not predict the presence of pathogens. Further research needs to be
performed on the relationship between indicator organisms and pathogens before current
microbial standards can reliably be used to predict water safety. Failing refinement of indicatorpathogen correlation, testing methods for human pathogens need to be developed which allow
for rapid and inexpensive detection so that indicator organisms need no longer be used.
49 3.2 INTRODUCTION
Surface water is widely used for irrigating fruit and vegetable crops in the United States
(Suslow 2010). Compared to municipal and groundwater sources, its microbial quality is highly
variable because it is open to sudden and unexpected environmental contaminations (Bihn and
Reiners 2011). Unlike recreational surface waters, there has been very little research into the
microbiology of surface waters used for irrigation. In agricultural food production, unlike
municipal and well water standards which are firmly established, there are no nationally enforced
standards for surface water used for crop irrigation. Some produce industry marketing groups,
government agencies, and private audit firms have developed microbial standards for surface
water quality. These standards are based on an assumed relationship between populations of
indicator microorganisms and human pathogens.
Previous studies have investigated these relationships, however they have often focused
on water sources already known to be contaminated with pathogens (Caplenas and Kanarek
1984; Harwood et al. 2004; Walters et al. 2007) or on only one or two water sources (Ferguson et
al. 1996; Lemarchand and Lebaron 2003; Walters et al. 2007; Haley et al. 2009; Shelton et al.
2011). These restrictions may limit extrapolation of the results to a wide number and variety of
surface water locations. This study was developed in such a way that it encompassed as many
water sources and as much variety in the types of water sources and physical characteristics as
possible. It was the goal with such a broad scope this study to capture large-scale trends that are
true across all water sources. Additionally, methods were developed to approximate the
experiences a Pennsylvania farm would have if they were to perform a water test on their own.
Since the overall goal of this study is to develop protocols to help Pennsylvania farmers to
understand and ease water testing, it is more important to understand the results that farmers
50 would get, as opposed to performing a highly controlled study. In this chapter, the relationship
between populations of indicator organisms and human pathogens, and the physical and
environmental characteristics of select Pennsylvania surface water sources is investigated in
order to determine if water quality standards can effectively predict the safety of surface
irrigation water.
3.3 MATERIALS AND METHODS
3.3.1 Selection of Sampling Sites
Pennsylvania growers who use surface water to irrigate fruit and vegetable crops were
identified through a follow up survey after a 2009 Penn State Cooperative Extension Good
Agricultural Practices workshop. Methods for this survey were kept as close to current water
testing standard methods and audit recommendations to simulate water testing which would be
performed on a Pennsylvania farm. One of the requirements of the standard methods for surface
water testing is that water samples be processed within 6 hours of sample collection (APHA
2005). Therefore, growers within a 6 hour driving distance of the testing facility were contacted
to determine their interest in participating in the project. Permission was granted by 39 growers
to survey their surface water.
FIGURE 3.1. Location of farms from which surface water samples were collected during 2010
and 2011.
51 3.3.2 Collection and Transportation of Water Samples
Samples were taken in accordance with recommendations to the USDA auditor training
manual (USDA 2007) that surface water be sampled at the beginning, middle, and end of the
growing season. Samples were collected from each sampling site three times during each
summer, approximately once each in June, July and August, as logistics allowed. One-liter grab
samples were collected in sterilized HDPE bottles (Nalgene, Rochester, NY, USA). A 3-meter
long sampling pole (WhirlPak, Fort Atkinson, WI, USA) was modified so that the sampling
bottle could be inverted during sampling. This allowed for the collection of water at
approximately the same location and water depth as the irrigation equipment intake source, and
excluded water from the surface of the water source, which may have different bacterial
populations (APHA 2005). Samples were immediately stored in a portable electric cooler
(Koolatron, Brantford, ON, Canada) at 4°C until testing began. Samples were analyzed in the
food microbiology laboratory in Luerssen Building at The Pennsylvania State University – Berks
Campus within 6 hours of collection.
3.3.3. Measurements Obtained by Onsite Observation
The type of water source from which samples were obtained was described using
definitions from Bates and Jackson (1987). Definitions of terms used to characterize each water
source are shown in Table 3.1. Relative scales describing the degree of water movement, algae
growth, and sunlight were developed so that observations across the two sampling years would
be consistent. At each sampling site, a value from 0 to 3 was assigned for each characteristic.
Information on farm size, crops grown, use(s) of the water source, depth of water source,
52 whether the source was man-made or natural, and upstream activities was collected from the
grower.
TABLE 3.1. Definitions of terms used to describe characteristics of water sources. Category
Term
Definition
An intermittent flow of water which may not exist year round
Type of
Creek
Water
Stream An unbroken flow of water which moves by gravity downward in
Source
a narrow and defined channel
A perennial watercourse, a large flow of water in a highly defined
River
channel
A flow of water which moves within a man-made channel
Canal
Inland body of water that fill depressions in the earth
Lake
Smaller than a lake, where the water temperature is the same at
Pond
the surface and bottom of the body of water.
Not moving at all
Movement
0
of Water
Very small current, only visible particulates (twigs, leaves) move
1
Source
Water visibly moves
2
Water is fast moving “white” water
3
No algae growth
Algae
0
Growth
Small amount of growth around the edges of the water body
1
There is visible algae growth throughout the pond
2
Water is almost not visible through the algae growth
3
Completely covered by shade at all times
Sunlight
0
Less than half of the water source receives sunlight for less than
1
half of the day
More than half of the water source receives sunlight for less than
2
half of the day
More than half of the source received sunlight for more than half
3
of the day
3.3.4. Measurement of Physical Characteristics of Water Sources
All water samples were analyzed for dissolved oxygen, water and air temperature, pH,
conductivity, turbidity and precipitation received. Measurements for dissolved oxygen and water
temperature were taken at the sampling site using a Orion 3 Star Portable Dissolved Oxygen
Meter (Thermo Scientific, Lenexa, KS, USA). The probe was attached to the end of the sampling
53 pole so that readings could be taken from approximately the same location and depth as the water
sample.
Conductivity, pH, and turbidity were analyzed in the laboratory. Before laboratory
analyses were conducted, water samples were shaken vigorously 25 times in 30 cm (1 ft) arc in 7
seconds, and tested within 15 minutes as outlined in the Bacteriological Analytical Manual
(BAM; FDA 2002b). pH was tested using a Mettler Toledo SevenEasy pH meter (Mettler
Toledo, Columbus, OH, USA). Conductivity was measured using a YSI Environmental
Conductivity Meter (YSI Incorporated, Yellow Springs, OH, USA) and turbidity was measured
using a HACH 2011P Turbidimeter (HACH Lange, Dusseldorf, Germany).
Air temperature and precipitation data were retrospectively found using historical
meteorological data accessed from Weather Underground (www.wunderground.com).
Precipitation was recorded for both the day of sampling (sampling day precipitation) and for the
total from the day of sampling and the three previous days (accumulated precipitation).
Meteorological data was obtained by Weather Underground from Automated Surface
Observation Systems (ASOS) sites located at airports and maintained by The Federal Aviation
Authority (FAA). Observations at these stations are taken hourly, or more often when conditions
are poor. The closest weather station to each sampling location was determined using zip codes.
3.3.5. Microbial Analysis
Indicator Organisms
Samples were analyzed for six microbial indicators: total plate count (TPC),
Enterobacteriaceae, coliforms, fecal coliforms, E. coli, and enterococci. After agitating samples
as previously described, dilutions from 100 to 10-2 CFU/mL were made by adding 1 mL of the
54 water sample to 9 mL Buffered Peptone Water (BPW, Becton Dickinson and Company (BD),
Sparks, MD, USA). Total plate count determinations were made on Aerobic Plate Count
Petrifilm™ (APC Petrifilm™, 3M, St. Paul, MN, USA) and incubated for 24 hours at 37°C.
Enterobacteriaceae were enumerated on Enterobacteriaceae Petrifilm™ (3M) and incubated at
37°C for 24 hours. Coliform and E. coli were enumerated simultaneously on E.coli/Coliform
Petrifilm™ (3M), which was incubated for 24 hours at 37°C. Fecal coliforms were enumerated
using Coliform Petrifilm™ (3M) held in a humidified incubator at 42°C for 24 hours. All
samples were plated on Petrifilm™ duplicate.
During the first month of sample collection, enterococci were enumerated by spread
plating 0.3 mL from each dilution on KF streptococcus agar (BD) supplemented with 1%
Triphenyl Tetrazolium Chloride (TTC; BD). Because enterococci were not recovered or were at
levels too low to enumerate (<1 CFU/mL) using spread plating, the procedure was changed. Ten
milliliters and 100 mL of sample were filtered through a sterile 47 mm cellulose membrane with
a pore size of 0.45µm, (PALL, Port Washington, NY, USA) using a sterilized membrane
filtration unit (PALL). The filter was then placed on KF streptococcus agar supplemented with
1% TTC solution and incubated at 37°C for 24 hours. Samples were filtered and plated in
duplicate.
Pathogens
Two different methods were used to analyze water samples for human pathogens, one in
each year of sampling. In 2010, samples were analyzed using the standard methods for water
analysis (APHA 2005). This method attempted to enumerate levels of pathogens in a water
source. Because this method failed to recover any pathogens from the water sources sampled,
55 research was conducted to determine alternative pathogen detection methods (Chapter 4). In
2011, the method used was based on these results. This new method was only able to detect the
presence or absence of pathogens in a sample; it could not be used for enumeration.
In 2010, four human pathogens, E coli O157:H7, Salmonella spp. , Yersinia
enterocolitica, and Shigella spp., were enumerated using membrane filtration. A dilution of 10-2
was made by adding 1 ml of the water sample to 99 mL of BPW. Volumes (100mL) of 100
CFU/mL and 10-2 CFU/mL dilutions of the surface water sample were filtered through a 47 mm
cellulose membrane with a pore size of 0.45 µm using a sterilized membrane filtration unit. After
filtration, the membranes were placed onto different selective agars for each pathogen. For E.
coli O157:H7, membranes were plated on Sorbitol MacConkey agar (SMAC agar; BD)
supplemented with Cefixime and Tellurite (CT supplement; BD) and incubated for 24 hours at
37°C. Membranes were placed onto Yersinia Selective agar (BD) supplemented with Cefsulodin
and Novobiocin (BD) and incubated for 24 hours at 37°C to identify Yersinia enterocolitica.
Both Salmonella spp. and Shigella spp. were detected by plating the membranes on Xylose
Lysine Deoxycholate agar (XLD agar; BD) and incubating at 37°C for 24 hours. Filtration and
plating was conducted in duplicate for each pathogen. Presumptive Shigella spp. and Yersinia
enterocolitica were confirmed using Wellcolex Colour latex agglutination kits as described by
the manufacturer (Thermo Scientific). Presumptive colonies were isolated and stored at room
temperature on nutrient agar slants topped with mineral oil.
In 2011, the method was altered to include an enrichment step which allowed for the
recovery of injured organisms that might increase levels of pathogens detected. Logistically it
was not feasible to test for all four pathogens using new methods, so pathogen detection was
focused the two most-implicated pathogens in produce related outbreaks.
56 Salmonella spp. was detected by modifications of methods described by Johnson et al.
(2003), Baudart et al. (2000), and Haley et al. (2008). A 90 mL sample was added to 10 mL of
10X concentrated BPW and incubated at 37°C. After 24 hours, 0.1 mL of this enriched sample
was added to 9.9 mL of Rappaport Vassiliadis Broth (EMD Millipore, Billerica, Massachusetts,
USA) and incubated at 42°C for 24 hours. After incubation, the sample was spread plated in
triplicate onto the surfaces of both Brilliant Green agar (BG; HIMEDIA, Mumbai, India) and
Xylose Lysine Deoxycholate agar (XLD agar; BD) and incubated for 24 hours at 37°C. In order
to reduce false Salmonella spp. positives, any suspect colonies from the XLD agar were streaked
onto BG and XLD agars, and any suspect colonies on BG agar were streaked on XLD and BG
agars. All plates were then reincubated for 24 hours at 37°C. Presumptive colonies were isolated
and stored on nutrient agar slants at -80°C.
Detection of E. coli O157:H7 was performed using a modification of the methods
outlined in Johnson et al. (2003) and Weagant and Bound (2001). A 90 mL aliquot of the surface
water sample was added to 10 mL of 10X concentrated E. coli Broth with novobiocin (ECNovo;
EMD Millipore) and incubated at 42°C for 24 hours. A 0.25 mL aliquot of this sample was
spread plated in triplicate onto the surface of Sorbitol MacConkey agar (SMAC agar; BD)
supplemented with Cefixime and Tellurite (CT supplement; BD) and incubated at 37°C for 24
hours. After incubation, suspect colonies were streaked onto E. coli O157:H7 Chromogenic agar
(CHROMagar, Paris, France) and incubated for 24 hours at 37°C. Presumptive colonies were
isolated and stored on nutrient agar slants at -80°C.
57 PCR Analysis
Polymerase chain reaction (PCR) was used to confirm presumptive Salmonella spp. and
E. coli O157:H7 isolates. Isolates from both years of the surface water survey were regrown in
BPW. Samples were centrifuged for 2 minutes at 12,000Xg and the pellet of concentrate cells
was collected for DNA extraction. Cells were re-suspended cells in 200µL of water, boiled at
100°C for 10 minutes and then centrifuged for 2 minutes at 12,000Xg. A 1 µL aliquot of
extracted DNA from cultures of E. coli O157:H7 ATCC 43895 (American Type Culture
Collection, Manassas, VA, USA) and Salmonella Enteritidis ATCC 13076 (American Type
Culture Collection) were used as a positive control. The primer sequences for E. coli and
Salmonella targeted an O-antigen and invasion gene respectively as described in Table 3.2. PCR
reagents were obtained from Epicentre (Madison, WI, USA).
TABLE 3.2. Primers and PCR conditions used to confirm pathogen presence.
Annealing Number
of
Temp
Target
Primer
o
Cycles
( C).
Organism
Target
Primer Sequence 5’  3’
CTGCCGCAGTGTTAAGGATA
62
30
Salmonella SPII
spp.
Invasion
Gene
CTGTCGCCTTAATCGCATGT
(hilA)
TCGAGGTACCTGAATCTTTC
O62
30
E. coli
CTTCTGT
O157
antigen
flippase ACCAGTCTTGGTGCTGCTCT
(wzx)
GACA
Reference
Guo et al.
2000
Debroy et
al. 2011
For Salmonella isolates, primers (6µL each) were mixed with dNTP solution (1.6µL),
MasterAmp Taq 10X PCR buffer (2µL), MasterAmp Taq DNA Polymerase (0.1µL), MgCl2
Solution (1.2µL), and microfiltered, deionized water (2.1µL) to a final volume of 19µL. Isolated
DNA (1µL) was added to the master mix. PCR amplification was performed on a DNA thermal
58 cycler (Eppendorf, Hamburg, Germany) by initial denaturation at 94°C for 15 minutes followed
by 30 cycles of denaturation at 94°C for 30 seconds, primer annealing at 62°C for 1.5 minutes,
and extension at 72°C for 1.5 minutes, followed by a final extension for 10 minutes at 72°C.
For E. coli O157:H7 presumptive isolates, primers (1µL each) were mixed with dNTP
solution (1.6µL), MasterAmp Taq 10X buffer (2µL), MasterAmp Taq DNA Polymerase (0.1µL),
MgCl2 Solution (1.2µL), and microfiltered, deionized water (12.1µL) to a final volume of 19µL.
Isolated DNA (1µL) was added to the master mix. PCR amplification was performed as
described for Salmonella isolates.
PCR amplicons were analyzed using gel electrophoresis on 1% agarose gel (MoBio,
Carlsbad, CA, USA) in 1X Tris Borate EDTA Buffer (TBE buffer; EMD Millipore). The gel was
stained with ethidium bromide (MoBio) and imaged on an EC3 bioimaging system (UVP
Bioimaging, Upland, CA, USA).
3.3.6. Statistical Analysis
Statistical analysis was performed using the Statistical Analysis Software (SAS) system
version 9.3 (SAS Institute Inc, Cary, NC, USA). Before statistical analysis was performed,
microbial counts were transformed into log10 CFU. When transforming data to log10 values,
samples with microbial indicator levels below the detection limit were assigned the same value
as the lowest detectable value (1 CFU/mL or 0 log10). Tukey tests were performed to determine
if significant differences existed between indicator organism levels in samples which tested
positive or negative for Salmonella. Pearson correlation coefficients were determined to describe
the relationship between microbial indicator organisms and physical characteristics measured.
59 3.4 RESULTS
A total of 153 water samples taken from 39 unique sites were sampled over the two years
of the survey within the southeast region of Pennsylvania. This area was roughly bounded by
Interstate highway 80 and the southern border of the state, and by the Susquehanna River and the
eastern border of the state (Figure 3.1). Eight of the sampling sites were on Amish or Mennonite
farms and four additional sites were self-described as organic farms.
Table 3.3 lists the number of water samples taken from each sampling site in each year
and site characteristics. Every effort was made to sample sites three times per growing season;
however, some sites were sampled in 2010 but not re-sampled in 2011. This was because some
growers elected not to participate in the second year. In 2010, three sites were sampled only once
during the growing season. This was because during the first month of sampling, it was decided
that these three sites could not be consistently returned to laboratory within the required six hour
time limit. During the 2011 growing season, two sampling locations were sampled only once
because these water sources had dried up as a result of summer drought conditions. The most
commonly sampled water source types were ponds (14) and creeks (12), followed by rivers (7),
streams (2), open wells (2), lakes (1), and springs (1). On a scale from 0 to 3, the mean water
movement was 0.92 indicating that most water sources had no or barely visible current. The
mean algae growth was 0.64 indicating very small amount of algae growth around the edges of
the water source. The highest mean value was for the amount of sunlight received by a water
source at 2.05, indicating that the majority of water sources received sunlight to more than half
of the source.
60 TABLE 3.3. Characteristics of water sources sampled in 2010 and 2011.
Farm
1
2
3
4
5
6
7
8
9
Number
Samples
in 2010
2
3
3
3
3
3
3
3
3
Number
Samples
in 2011
0
0
0
0
3
3
0
3
3
Water
Type
River
Pond
Pond
River
Lake
Stream
Pond
Creek
Pond
Movement
(0-3)
2
0
1
2
1
2
0
3
0
Algae
(0-3)
0
0
1
0
1
0
1
0
2
Sunlight
(0-3)
2
3
3
3
3
2
3
1
3
10
3
0
Pond
0
3
3
11
12
13
14
3
1
1
3
0
0
0
0
Spring
Creek
Pond
Creek
2
2
0
1
0
0
0
0
2
1
3
1
15
3
0
Creek
2
0
1
16
17
18
19
20
21
22
3
3
3
3
3
3
3
0
0
0
3
0
0
0
Creek
Creek
Pond
Pond
Well
Well
Creek
1
1
0
0
0
0
2
0
1
0
1
0
0
0
2
1
3
3
0
0
3
23
3
0
Creek
2
0
1
24
3
3
Creek
2
0
0
25
26
27
28
3
3
3
3
3
3
1
3
Pond
Pond
River
River
0
0
1
2
1
2
1
0
3
3
2
2
29
3
3
Stream
1
1
1
30
3
3
Creek
1
0
3
31
3
3
Pond
0
0
2
32
33
34
3
3
0
3
3
3
Pond
Pond
Pond
0
0
0
1
3
2
3
3
3
35
0
1
Creek
0
0
0
36
37
0
0
3
3
Creek
River
1
2
1
1
3
1
38
0
3
River
1
1
3
39
0
3
River
1
1
1
Crops
Corn
Tomatoes, potatoes
Berries
Tomatoes, corn
Corn
Tomatoes, corn, peppers, pumpkins, squash
Corn
Treefruit
Treefruit
Corn, pumpkins, cabbage, zucchini,
cucumbers, tomatoes, leafy greens
Tomatoes, potatoes, squash, radishes
Treefruit
Tomatoes, pumpkins, cucumbers, zucchini
Corn, broccoli, cabbage
Tomatoes, potatoes, corn, peppers, berries,
squash, cucumbers, onions, beans, melons,
Potatoes
Potatoes
Potatoes
Potatoes, corn, cucumbers, beans
Beans, onions
Tomatoes, corn, squash
Peppers, potatoes, onions, beans
Tomatoes, corn, peppers, cucumbers,
broccoli
Tomatoes, potatoes, peppers, cucumbers,
leafy greens
Treefruit, berries, pumpkin
Berries, corn, treefruit, squash, leafy greens
Corn
Corn
Tomatoes, peppers, leafy greens, squash,
corn
Squash, tomatoes, potatoes, onions,
cucumbers, melons
Squash, tomatoes, potatoes, onions,
cucumbers, melons
Treefruit, berries
Treefruit, berries
Berries, broccoli, squash, tomatoes, treefruit
Corn, radish, squash, pumpkins, tomatoes,
potatoes
Tomatoes, squash, potatoes, berries, corn
Melons, squash, tomatoes, corn
Squash, zucchini, broccoli, peppers, onions,
potatoes, carrots
Squash, zucchini, broccoli, peppers, onions,
potatoes, carrots
61 The most common crops grown at the farms in the survey were corn (17 sites), potatoes
(15 sites), tomatoes (14 sites), and squash (14 sites). Approximately 69% of samples were
obtained from farms where a mixture of different crops was grown.
Summary statistics for indicator organism populations are shown in Table 3.4.
Populations ranged from undetectable to above the detection limit of the study (4.5 log10). As
expected, mean levels of indicator bacteria followed the hierarchy of bacterial classes (Figure
2.2), there were more Enterobacteriaceae than coliforms, more coliforms than fecal coliforms,
and more fecal coliforms than E. coli. The pH range of the water samples was 5.3 to 7.31,
indicating that none of the water sources sampled were basic. Conductivity had the largest range,
from 7.9 to 805 microsiemens/cm (µs/cm). Air temperature ranged from 17 to 36°C. Samples
were taken on the hottest day of 2010 where temperatures reached 39°C; however, samples were
taken in the morning to allow time in the afternoon for laboratory analysis.
Populations for total plate counts, Enterobacteriaceae, coliforms, fecal coliforms, E. coli,
and enterococci at all sampling locations are shown in Figures 3.2, 3.3, 3.4, 3.5, 3.6, and 3.7
respectively. Results showed high variability both between and within sampling sites. Within an
individual sampling location, results varied by as much as 4.5 log10 in one sampling year. No
significant difference was seen in levels of indicator organisms between samples collected in
2010 and samples collected in 2011.
Levels of indicator organisms found in the Pennsylvania surface waters sampled are
compared to current standards outlined in Chapter 2 (Tables 2.2 and 2.3). Figure 3.8 shows the
percentage of samples collected which would have failed current EPA standards (126 E. coli
CFU/100mL, 33 enterococci CFU/100mL), Pennsylvania EPA standards (200 fecal coliform
CFU/100mL), World Health Organization standards (1000 fecal coliform CFU/100mL), and
62 CCME standards (1000 coliform CFU/100mL). The data collected over both years shows that
between 44 and 68% of the water samples collected would have failed each of the six current
irrigation water standards.
In 2010, E. coli O157:H7, Salmonella spp., Shigella spp., and Yersinia enterocolitica,
were not detected in any of the water sources. Many plates were overgrown with background
microflora and individual colonies were difficult to observe. Presumptive colonies were observed
during plating; however, none could be confirmed as pathogens using agglutination tests or PCR.
This led to a re-evaluation of the method used to determine if pathogens were present (Chapter
4).
TABLE 3.4. Descriptive statistics for microbial indicator populations and physical
characteristics.
Total Plate Count
(Log10 CFU/mL) Enterobacteriaceae
(Log10 CFU/mL) Coliform
(Log10 CFU/ mL) Fecal Coliform
(Log10 CFU/mL) E. coli
(Log10 CFU/mL) Enterococci
(Log10 CFU/mL) pH
Conductivity (µs)
Turbidity (NTU)
Dissolved Oxygen (mg/L)
Air Temperature (°C)
Water Temperature (°C)
Sampling Day Precipitation
(inches)
Accumulated Precipitation
(inches)
Range
Mean
Median
Standard
Deviation
0 – 4.5 2.87 2.79 1.01 0 – 4.5 1.86 1.85 0.94 0 – 4.5 1.20 1.08 1.06 0 – 4.5 1.06 1.00 0.92 0 – 4.5 0.88 0.48 1.06 0 – 4.42 1.49 1.76 1.30 5.3-7.31
7.9-805
0-211
1.35-31.6
17-36
8.53-33.6
6.55
276.86
16.62
9.53
25.97
22.89
6.59
235.7
6.6
8.46
27
23.2
0.38
170.44
31.32
4.28
3.46
4.43
0-1.05
0.12
0
0.24
0-6.01
0.45
0.25
0.85
63 FIGURE 3.2. Results of total plate count analysis at each sampling location. X-axis points
represent each individual sampling location and show each of the between 1 and 6 samples
collected at that site.
FIGURE 3.3. Results of Enterobacteriaceae analysis at each sampling location. X-axis points
represent each individual sampling location and show each of the between 1 and 6 samples
collected at that site.
64 FIGURE 3.4. Results of total coliform analysis at each sampling location. X-axis points
represent each individual sampling location and show each of the between 1 and 6 samples
collected at that site.
FIGURE 3.5. Results of fecal coliform analysis at each sampling location. X-axis points
represent each individual sampling location and show each of the between 1 and 6 samples
collected at that site.
65 FIGURE 3.6. Results of E. coli analysis at each sampling location. X-axis points represent each
individual sampling location and show each of the between 1 and 6 samples collected at that site.
FIGURE 3.7. Results of enterococci analysis at each sampling location. X-axis points represent
each individual sampling location and show each of the between 1 and 5 samples collected at
that site.
66 Samples Which Failed Current Surface Water
Guidelines (%)
80
70
60
50
40
2010
30
2011
20
Both
Years
10
0
EPA
1
E. coli
126CFU/
100mL
EPA
2
E. coli
235CFU/
100mL
PA EPA
3
Fecal
Coliform
200CFU/
100mL
WHO
4
Fecal Coliform
1000CFU/
100mL
EPA
Canadian
5
6 CCME
Enterococci Total Coliform
33CFU/
1000CFU/
100mL
100mL
FIGURE 3.8. Percent of surface water samples collected which failed current surface water
microbial standards.
In 2011, E. coli O157:H7 was not isolated from any of the water sources; however,
Salmonella spp. were detected in 5 water samples, or 8.5% of the year’s samples. Of these five
Salmonella-positive samples, one was collected from a pond on an organic farm where a chicken
house was located adjacent to the water source, and two samples were collected from two plain
farms where in both cases cattle and horses had free access to the water sources. Two samples
were obtained from a pond located on a conventional farm where the water source was located
adjacent to a highway. Salmonella spp. was found at these sites across all three months of
sampling. A statistical difference in levels of any indicator organism between the five Salmonella
positive samples and the Salmonella negative samples was not seen. It is possible that the limited
sample size of Salmonella positive samples may have affected the ability to see a statistical
difference.
67 The results show that indicators are not good predictors for the presence of pathogens.
For instance, it has been suggested that generic E. coli is the best indicator of fecal contamination
and thus the safety of surface water (Suslow 2010); however, of the 79 water samples (52%)
which would have failed the E.coli standard of 126 CFU/100mL, only four also contained human
pathogens. Moreover, of the five water samples that tested positive for Salmonella spp., only one
would have failed all of the indicator standards (Table 3.5).
Table 3.6 shows the Pearson coefficients of correlation between each of the individual
indicator organisms. All correlations (p ≤ 0.05) were significant; however the highest correlation
coefficient value was between coliforms and E. coli. This may have been because they were
enumerated on the same petrifilm. The lowest correlation coefficient values were between
enterococci and each of the other indicator organisms. A weaker correlation is not unexpected
because enterococci is not located within the same family of bacteria as the other indicator
organisms.
TABLE 3.5. Comparison of indicator organism levels to current surface water standards in those
samples which tested positive for Salmonella.
Farm
9
9
38
32
31
Month of
Sampling
June
July
July
Aug
June
E. coli
126 CFU/
100mL
Failed
Passed
Failed
Failed
Failed
E. coli
235 CFU/
100mL
Failed
Passed
Failed
Failed
Failed
Standard
Fecal
Fecal
Coliform
Coliform
200 CFU/ 1000 CFU/
100mL
100mL
Passed
Passed
Failed
Passed
Failed
Failed
Failed
Failed
Passed
Passed
Enterococci
33 CFU/
100mL
Failed
Failed
Passed
Failed
Failed
Coliform
1000 CFU/
100mL
Failed
Passed
Failed
Failed
Failed
68 TABLE 3.6. Pearson correlations between microbial indicator organisms measured in the study.
Total Plate
Count
Pearson Correlation Coefficients; N=153
Prob > |r| under H0 Total Plate
Fecal
EnterobacCount
Coliform Coliform teriaceae
E. coli
1.00000
0.59763*
1.00000
<.0001
0.65017* 0.77539*
1.00000
Coliform
<.0001
<.0001
0.69936* 0.64300* 0.80329*
Enterobacteriaceae
<.0001
<.0001
<.0001
0.61379* 0.74555* 0.91780*
E. coli
<.0001
<.0001
<.0001
0.18767* 0.34483* 0.36257*
Enterococci
0.0427
<.0001
<.0001
* indicates significant difference at α=0.05
Enterococci
Fecal
Coliform
1.00000
0.75279*
<.0001
0.31789*
<.0001
1.00000
0.38067*
<.0001
1.00000
Table 3.7 shows the Pearson coefficients of correlation between indicator organisms and
physical characteristics. All indicator organisms were significantly correlated (p ≤ 0.05) with
water pH. Negative correlation coefficient values for pH show that levels of indicator organisms
tend to be higher in slightly acidic water sources. Conductivity showed significant correlation to
fecal coliforms and coliforms, whereas dissolved oxygen only showed correlation to enterococci.
No correlation was seen between any of the indicator organisms and levels of precipitation on
the day of sampling, or the accumulated level of precipitation for the three days prior to
sampling. Correlations were also not significant between turbidity, air temperature, and water
temperature and any indicator organism.
69 TABLE 3.7. Pearson correlations between microbial indicator organisms and physical
characteristics measured in the study.
Pearson Correlation Coefficients; N=153
Prob > |r| under H0 Total
Plate
Fecal
EnterobacCount
Coliform Coliform
teriaceae
-0.33671* -0.29148* -0.37345*
-0.37592*
pH
<.0001
0.0003
<.0001
<.0001
0.09328
0.18230*
0.20393*
0.13987
Conduc.
0.2514
0.0241
0.0115
0.0846
-0.09450
-0.07649
-0.10111
-0.15859
Dissolved
Oxygen
0.2453
0.3473
0.2137
0.0502
0.01122
-0.00038
-0.01385
-0.01971
Sampling Day
Precipitation
0.8905
0.9963
0.8651
0.8089
-0.02417
0.06300
0.09711
0.04229
Accumulated
Precipitation
0.7668
0.4391
0.2324
0.6037
0.03004
0.03566
0.00030
0.04951
Turbidity
0.7125
0.6617
0.9970
0.5434
-0.09534
-0.06283
-0.14270
-0.10464
Temp. Air
0.2411
0.4404
0.0785
0.1980
-0.01154
-0.10463
-0.07574
-0.04730
Temp. Water
0.8874
0.1980
0.3521
0.5615
* indicates significant difference at α=0.05
E. coli
-0.35375*
<.0001
0.13536
0.0953
-0.10539
0.1948
-0.04430
0.5866
0.06336
0.4365
0.03275
0.6878
-0.09132
0.2616
-0.04366
0.5920
Enterococci
-0.19630*
0.0339
0.00785
0.9331
-0.18927*
0.0410
0.05968
0.5227
0.12676
0.1732
0.09872
0.2896
0.12353
0.1845
-0.03809
0.6835
For each indicator organism, population levels were compared with levels for water
movement (Figure 3.9a), algae growth (Figure 3.9b), and amount of sunlight received (Figure
3.9c). The data show that none of the microbial levels was significantly affected (p ≤ 0.05) by
these environmental parameters.
70 a
b
c
FIGURE 3.9. Average indicator bacterial populations in water sources at each level of water
movement (a), algae growth (b), and sunlight (c).
3.5 DISCUSSION AND CONCLUSIONS
Over the course of the study only five instances of pathogen contamination was observed.
This level is lower than in previous surface water studies and may have occurred because, in this
71 study, a wide variety of irrigation water sources was sampled (Ferguson et al. 1996; Polo et al.
1998; Lemarchand and Lebaron 2003; Ijabadeniyi et al. 2011; Shelton et al. 2011). Other surface
water studies focused on water sources that were known to be contaminated with pathogens or
which were close to potential sources of contamination, such as sewage treatment plants or cattle
farms. Our choice not to limit our study to high risk areas may have had an effect on limited the
number of pathogens found; however, it may also have given a more accurate overall portrayal
of the frequency of pathogen contamination in irrigation water in Pennsylvania. In addition, the
sampling sites in this survey were identified from attendees of a Penn State Good Agricultural
Practices extension workshop and growers self-selected into the study. This may have biased the
sites selected toward those farms which currently employ GAP, and therefore water safety,
practices.
The high variability observed within individual sampling sites indicates that the sample
timing may greatly affect whether a water source is deemed safe for use. In some surface water
standards based on recreational water regulations, rolling geometric means are used which
compare the average of the previous 5 water test results against the standard (Primus 2007a;
Primus 2007b; LGMA 2012). The use of this method may decrease failure rates by dampening
the effect of one outlier result. Were this method to be used in this study the failure rate would
have been lower.
High correlation values between E. coli, fecal coliform, and coliform ( r>0.74) suggest
that an increase in one of these organisms would correspond to an increase in the other
organisms. Because of this, one single organism could be chosen as an indicator of fecal
contamination instead of using multiple indicators as benchmarks (e.g. total coliforms and E.
coli). Until further studies can be performed to investigate the relationship between indicator
72 organisms and pathogens, E. coli should continue to be used because it is the only true indicator
of fecal contamination.
Approximately 50% of all water samples collected over the two years would have failed
to meet all of the current surface standards cited in this study (Figure 3.7). A comparison of
indicator levels to current standards may not be completely accurate because standards are based
on a per 100 mL basis, whereas results were reported on a per mL and extrapolated to an 100 mL
volume. Nevertheless, this high level of failure, without an accompanying detection of human
pathogens, indicates that the current water quality standards may be unreasonably strict, and may
limit the use of water sources which are in reality safe for use. This may cause financial burden
on growers who do not have other sources of irrigation water. On the other hand, four of the five
water samples that tested positive for Salmonella spp., would have passed at least one of the six
standards. This shows that using current irrigation water standards could allow unsafe, pathogenladen water sources to be used for irrigation. Thus the poor ability of indicator organisms to
predict the safety of a water source brings into question the appropriateness of using any of these
standards to increase food safety. More research needs to be done to determine the relationship
between human pathogens and indicator organisms in order to develop standards that can
accurately predict the safety of surface water sources. Alternatively, direct testing methods for
human pathogens should be made easier, faster, and less expensive so that indicator organisms
need no longer be used.
The correlation between indicator organisms and pH has not been reported in other
surface water surveys; however, only Carter et al. (1987) attempted to determine a correlation
and he reported no significant effect of pH on pathogen or indicator levels. In this survey the
authors studied only two water sources, therefore it is possible their correlations were limited by
73 the small sample size. Alternatively, it is possible that this effect was a product of the way pH
was measured. It has been shown that the pH of a water sample can change over time. During
this study water samples were collected and transported to the laboratory before pH was tested.
Measuring pH after this delay may have affected the accuracy of the pH results, and therefore
may account for finding a relationship to indicator organism populations which has not been seen
before. This relationship between pH and indicator organisms should be further investigated to
determine if pH directly affects indicator organism growth and survival. Perhaps this could
determine if this correlation could be be exploited to identify water sources which pose an
increased risk to public safety. We found the conductivity was only correlated to fecal coliforms
and coliforms, and dissolved oxygen to enterococci, whereas air and water temperature were not
correlated to any organisms. Previous surface water surveys have also looked at the physical
characteristics of water sources. Carter et al. (1987) reported that water temperature was
correlated to levels of fecal coliform, fecal streptococci, and total plate count, but not coliforms.
They also demonstrated that conductivity affected total coliforms and fecal coliforms, but not
fecal streptococci or total plate count. Correlations between microbial populations and turbidity
have also been investigated with conflicting results. Horman et al. (2004) reported a significant
correlation (p ≤ 0.05) correlation between turbidity and fecal coliform, E. coli, and total plate
count; whereas (Ijabadeniyi et al. 2011) only reported a significant correlation to enterococci, but
not to total plate count, coliforms, fecal coliforms, or E. coli. These differences in correlations
between surface water survey indicates that further research needs to be performed to understand
the role of the characteristics in the microbiology of surface water.
Correlations were not seen between indicator organisms and the other physical
characteristics tested. This may have occurred due to the experimental design that was chosen.
74 The decision was made for this study to be as broad a study as possible, capturing as many water
sources as possible and approximating farmers’ experiences. This choice meant that an effort
was not made to encompass the full range of each physical characteristic. It is possible that
without studying sources which cover a broad range for each characteristic, trends could have
been missed.
Previous surface water studies have reported a positive effect of levels of precipitation
and both indicator and pathogenic bacteria in surface waters (Ferguson et al. 1996; Steele et al.
2005; Haley et al. 2008); however, we found no correlation between levels of any of the
indicator organisms and precipitation either on the day of sampling, or accumulated precipitation
for the three days prior to sampling. It is possible that no correlation occurred because samples
were, by chance, not taken following large precipitation events. In fact, the highest level of
precipitation recorded on a sampling day throughout the survey was only 1.05 inches. This
amount may have been too low to capture microbial changes which might possibly occur after
larger precipitation events. It is also possible that because many sites were sampled on the same
day, the range of precipitation levels may not have been large enough to detect significant
differences in microbial levels. Further research involving more samples that follows an
individual site over an entire growing season and may be helpful to fully understand precipitation
events’ influence on microbial populations.
Although this survey collected more samples and studied more water sources than any of
the other surveys cited, it may be the case that not enough samples were obtained to determine
significant effects for each of the variables and their interactions. In order to fully understand the
risks of surface water and appropriate sampling strategies, a larger survey, which includes more
sampling sites and more repetition of samples would be needed. In addition, individual studies
75 should be performed to follow the effect of one variable on microbial populations more
frequently at one location over multiple growing seasons. Such studies may help to clarify the
environmental effects on microbial populations. This information will be useful to farmers in
helping them decide when to use irrigation waters on their crops.
76 CHAPTER 4. EVAULATION OF METHODS FOR DETECTING HUMAN
PATHOGENS IN SURFACE WATER SAMPLES
4.1 ABSTRACT
In Chapter 3, APHA standard methods for enumerating Salmonella and E. coli O157:H7
in surface water sources failed to yield any confirmed isolates in the 2010 surface water survey.
These methods included a membrane filtration step followed by plating techniques and did not
include a selective or recovery step. In this chapter, three methods each for detecting E. coli
O157:H7 and Salmonella were compared; the membrane filtration method used in the 2010
water survey and two new methods which utilized concentrated selective broths and differential
agars confirmed by latex agglutination. Surface water samples were inoculated with pure
cultures of E. coli O157:H7 or Salmonella at various inoculation levels, and pathogen recovery
was determined using each method. The most effective method for recovering Salmonella
included the use of 10X concentrated Buffered Peptone Water, Rappaport-Vassiliadis broth, and
Brilliant Green and XLD agars. The most effective method for recovering E. coli O157:H7
included 10X concentrated ECNovo broth, SMACCT and Chromogenic agar. For both of these
methods it was recommended that a final PCR step should replace agglutination to confirm
presumptive positive isolates.
77 4.2 INTRODUCTION
In Chapter 3, water sampling during the 2010 surface water survey in 2010 revealed
some issues with using membrane filtration and plating alone to identify pathogens. Many of the
water samples contained high levels of background microflora which often created a lawn of
bacteria that might have obscured any pathogens which may have been present. This may have
occurred because the method did not include a selective step to reduce background microflora
levels. This led to many false positives because of inadvertent counting of background, nonpathogenic bacterial colonies. In addition, this method did not contain a recovery step which may
be important for surface water, because many pathogens exist in the viable but non-culturable
state (Santo Domingo et al. 2000). It is possible that without a recovery step, injured or viable
but non-culturable pathogens may not have been counted.
The benefit of the 2010 method was that it allowed for bacterial enumeration; however,
the issues identified in this technique may have actually impeded enumeration. Due to the fact
that E. coli O157:H7 and Salmonella can infect at low doses (Blaser and Newman 1982;
Alsanius 2010) and because they are capable of growing on plant surfaces even when low levels
are transferred (Beuchat 2002; Cooley et al. 2003; Warriner et al. 2003; Jabasone et al. 2005;
Schikora et al. 2008), very low levels of these pathogens have the potential to cause harm. Given
this premise, the use of tests to determine presence or absence of human pathogens instead of
those which enumerate actual pathogen levels can be an acceptable way to evaluate food safety
risks.
Therefore, the objective of this chapter was to determine if enrichment methods to detect
the presence or absence of pathogens in surface water may address all the issues faced in 2010.
78 The new methods included recovery, selective, and differential steps, as well as a serological
confirmation.
4.3 MATERIALS AND METHODS
4.3.1. Selection and Preparation of Water Samples
Surface water samples were obtained from three sites in Centre County, Pennsylvania,
two from irrigation ponds on the Penn State agricultural research farm at Rock Springs, and one
from a river used for irrigation on a commercial farm in Centre Hall, Pennsylvania.
Cultures of E. coli O157:H7 ATCC 43895 (American Type Culture Collection,
Manassas, VA, USA) and Salmonella Enteritidis ATCC 13076 (American Type Culture
Collection) were grown in Buffered Peptone Water (BPW; Becton Dickinson and Company
(BD), Sparks, MD, USA) at 37°C for 24 hours to 108 CFU/mL. Serial dilutions were made by
adding 1 mL of culture to 9 mL of BPW to obtain a range of pathogen levels from 100 to 107
CFU/mL. One mL of each dilution was added to 99 mL of surface water to obtain samples with
pathogen levels between 10-2 and 105 CFU/mL. Surface water that was not inoculated with
pathogens served as a negative control. The positive control was sterile BPW inoculated with the
same level of pathogens as the surface water samples. Each set of pathogen-inoculated surface
waters and controls were subjected to three methods for determining pathogen presence; two
new methods as well as the membrane filtration method used during the 2010 surface water
survey.
4.3.2. Salmonella Detection Methods
The three analytical methods for Salmonella are diagrammed in Figure 4.1.
79 Salmonella Method 1
This method (Figure 4.1a) is based on the standard methods published by the American
Public Health Association (APHA 2005) and was used for the 2010 survey samples (Chapter 3).
Bacteria in surface water samples were dispersed by shaking vigorously 25 times in 30 cm (1 ft)
arc in 7 seconds, and tested within 15 minutes as outlined in the Bacteriological Analytical
Manual (FDA 2002b). Aliquots of 100 mL of 10-2 and 101dilutions of surface water samples
were filtered through a 47 mm cellulose membrane with a pore size of 0.45µm (PALL, Port
Washington, NY, USA) using a sterilized membrane filtration unit (PALL). Each membrane
filter was then aseptically placed on Xylose Lysine Deoxycholate agar (XLD agar; BD) and
incubated at 37°C for 24 hours. Presumptive isolates were confirmed using the Wellcolex Colour
Salmonella Latex Agglutination kit per manufacturer’s directions (Thermo Scientific, Lenexa,
KS). A confirmed positive agglutination showed definitive agglutination in the sample and no
agglutination in the negative control.
Salmonella Method 2
Salmonella was detected using a modification of methods described by Johnson et al.
(2003), Baudart et al. (2000), and Haley et al. (2008) (Figure 4.1b). Initial enrichment was
performed by adding 90 mL of the sample to 10 mL of 10X concentrated BPW and then
incubating at 37°C. After 24 hours, a secondary enrichment and selective step was conducted by
adding 0.1 mL of the enriched sample to 9.9 mL of Rappaport Vassiliadis Broth (RV broth;
EMD Millipore, Billerica, Massachusetts, USA) and incubating at 42°C for 24 hours. After
incubation, 0.25 mL was spread plated in triplicate on both Brilliant Green agar (BG agar;
HIMEDIA, Mumbai, India) and XLD agar and incubated for 24 hours at 37°C. Presumptive
80 colonies from XLD agar were streaked onto both XLD and BG agars and those on BG agar were
streaked onto BG and XLD agars and then re-incubated. Isolates that were presumptively
positive on both media were confirmed using the Wellcolex Colour Salmonella Latex
Agglutination kits per manufacturer’s directions.
Salmonella Method 3
Salmonella was detected using a modification of the procedure from Johnson et al. (2003)
(Figure 4.1c). This method is identical to Method 2 except for the difference in the broth used.
After the initial enrichment in concentrated BPW, 0.1 mL of the sample was transferred to 9.9
mL of Tetrathionate Broth (TBG broth; BD) supplemented with Brilliant Green and novobiocin
selective supplement (Sigma Aldrich, St. Louis, MO, USA) and then incubated at 37°C for 24
hours. The sample was then plated on XLD and BG agars and isolates positive on both media
were confirmed by agglutination as described in Method 2.
81 a) Method 1
b) Method 2
c) Method 3
FIGURE 4.1 Methods for detecting the presence of Salmonella in surface water samples.
82 4.3.3. E. coli O157:H7 Detection Methods
The three analytical methods for E. coli O157:H7 used to determine pathogen presence in
the inoculated surface water samples are diagrammed in Figure 4.2.
E. coli O157:H7 Method 1
This method (Figure 4.2a), which is based on the standard methods outlined by the
American Public Health Association (APHA 2005), was used for the 2010 survey samples.
Bacteria were dispersed by agitation as previously described. Aliquots of 100 mL volumes of
10-2 and 100 dilutions of surface water samples were filtered through a 47 mm 0.45µm cellulose
membrane using a sterilized membrane filtration unit. Each filter was then aseptically placed on
SMACCT agar and incubated at 37°C for 24 hours. Presumptive isolates were confirmed using the
Wellcolex Colour E. coli O157:H7 Latex Agglutination kit per manufacturer’s directions
(Thermo Scientific).
E. coli O157:H7 Method 2
E. coli O157:H7 was detected using a modification of the methods outlined in Johnson et
al. (2003) and Weagant et al. (1994) (Figure 4.2b). Initial enrichment was performed by adding
90 mL of this sample to 10 mL of 10X concentrated EHEC Enrichment Broth (EEB), a modified
Tryptone Soy Broth without novobiocin (Thermo Scientific) containing the selective
supplements Vancomycin, Cefixime, and Cefsulodin (VCC Suplement;Thermo Scientific) and
then incubated at 37°C. After 24 hours, 0.25 mL was spread plated in triplicate onto Sorbitol
MacConkey agar (BD) supplemented with Cefixime and Tellurite (CT supplement; BD). The
83 plates were incubated at 37°C for 24 hours. After incubation, presumptive colonies from
SMACCT agar were streaked onto E. coli O157:H7 Chromogenic agar (CHROMagar, Paris,
France) and incubated for 24 hours at 37°C. Presumptive colonies were confirmed using the
Wellcolex Colour E. coli O157:H7 Latex Agglutination kit per manufacturer’s directions.
E. coli O157:H7 Method 3
Detection of E. coli O157:H7 was performed using a modification of the procedure of
Johnson et al. (2003) and Weagant and Bound (2001) (Figure 4.2c). This method is identical to
Method 2 except for the initial enrichment broth and temperature of broth incubation. After
thorough mixing, 90 mL of the surface water sample was added to 10 mL of 10X concentrated E.
coli Broth with novobiocin (ECNovo, EMD Millipore) and incubated at 42°C for 24 hours. The
sample was then plated on SMACCT and isolated on E. coli O157:H7 Chromogenic agar and
confirmed by agglutination as described in Method 2.
84 a) Method 1
b) Method 2
c) Method 3
FIGURE 4.2. Methods for detecting the presence of E. coli O157:H7 in surface water samples.
85 4.4 RESULTS
In order to ensure the ability to select for pathogens in a variety of water sources, the
three methods were tested using surface water sources with a range of background microflora.
Initial microbial levels for all three water sources are shown in Table 4.1.
TABLE 4.1. Initial Total Plate Counts for Uninnoculated Surface Water Samples.
Sample
1
2
3
Location of Sample Collection
Rock Springs – Ag Progress Days
Exhibition Farm
Rock Springs – Agronomy Farm
Commercial Farm
Log10 Total Plate Count of
Uninnoculated Water Samples
1.7 CFU/mL
4.3 CFU/mL
5.6 CFU/mL
4.4.1. Salmonella Detection Methods
Table 4.2 shows the number of surface water samples at each inoculation dilution in
which confirmed Salmonella was found using each detection method
Salmonella was recovered from all BPW positive control samples, at all inoculation
levels. Salmonella was not recovered from any of the uninoculated surface water samples
(negative control), although many presumptive positives were found.
In order for a method to be designated as effective at any dilution level, Salmonella must
be recovered from all three water samples. Method 1 was only effective at recovering confirmed
Salmonella isolates at an initial inoculation of 104 CFU/mL, although one Salmonella isolate was
recovered from one water source at 103 CFU/mL. False positives were found at the lower
dilutions; however, these colonies were not confirmed by agglutination. It was difficult to
identify individual colonies using Method 1 because the plates were often overgrown and
86 colonies ran together. Improved recovery of Salmonella at higher inoculation levels may have
occurred because the pathogen outcompeted background microflora. Nevertheless, even at levels
where Salmonella was recovered, false positives were found using Method 1.
TABLE 4.2. Number of surface water samples at each inoculation level in which confirmed
Salmonella was found using each detection method.
Enumeration
Method
Enrichment Methods
Method 1
Method 2
Method 3
Inoculation
Water Sample
Water Sample
Water Sample
Level
1
2
3
1
2
3
1
2
3
(CFU/mL)
-2
10
+
10-1
0
+
+
+
+
10
+
+
+
+
+
101
+
+
+
+
+
+
102
+
+
+
+
+
+
+
103
4
+
+
+
+
+
+
+
+
+
10
+
+
+
+
+
+
+
+
+
105
+ colony confirmed by agglutination – no colonies confirmed by agglutination
Both of the enrichment methods resulted in improved detection of Salmonella at lower
inoculations compared with Method 1. Method 2 was more successful in detecting Salmonella at
lower inoculation levels than Method 3, because Method 2 effectively recovered Salmonella at
101 CFU/mL, whereas Method 3 effectively recovered Salmonella at 102 CFU/mL. Detection of
Salmonella using Method 2 did not appear to be affected by the level of background microflora
in the sample because it was isolated at the lowest dilution ( 10-1 CFU/mL) in the sample with
the total plate count (sample 3). Furthermore, recovery at the second lowest detection level was
seen in the sample with the lowest level of background microflora (sample 1).
A higher percentage of Salmonella isolates were confirmed by agglutination when the
enriched broth sample was spread plated onto both XLD and BG agars, than when only one agar
87 was used. More Salmonella colonies were isolated when broth was plated on BG agar and then
subsequently streaked onto XLD agar than when the order of the agars was reversed. In the
majority of samples in which Salmonella was positively confirmed, it was recovered when both
orders of agar were used. In many cases, due to the high level of background microflora and
colonies which had run together, more than one re-streak was required in order to obtain a pure
isolate of presumptive Salmonella.
4.4.2. E. coli O157:H7 Detection Methods
Table 4.3 shows the number of surface water samples at each inoculation dilution in
which confirmed E. coli O157:H7 was found using each dilution.
Confirmed E. coli O157:H7 isolates were recovered from all BPW positive control
samples. In the uninoculated, negative control samples, E. coli O157:H7 was only isolated from
one sample; however, this E. coli O157:H7 is not considered a confirmed isolate as it showed
inconclusive agglutination and therefore could not be either confirmed or excluded. In addition
to this isolate, many presumptive positive isolates were excluded through agglutination.
For the E. coli O157:H7 methods, similar to the Salmonella methods, an effective method
was defined as recovering E. coli O157:H7 from all three surface water samples. Method 1 was
effective at recovering E. coli O157:H7 at an inoculation level of 105 CFU/mL; however, it was
isolated from sample 2 only at 104 CFU/mL. This suggests that this filtration method was even
less effective at recovering E. coli O157:H7 than Salmonella Method 1. Many false positives
were found at all levels of inoculation using the membrane filtration method. As was the case
with the Salmonella filtration method, it was very difficult to identify individual colonies from
this method as the plates were often overgrown and colonies ran together.
88 TABLE 4.3. Number of surface water samples at each inoculation levels in which confirmed E.
coli O157:H7 was found using each detection method.
Enumeration
Method
Enrichment Methods
Method 1
Method 2
Method 3
Inoculation
Water
Sample
Water
Sample
Water
Sample
Level
(CFU/mL)
1
2
3
1
2
3
1
2
3
10-2
-1
10
+
+
100
1
+
+
+
10
+
+
+
+
+
102
+
+
+
+
+
103
+
_
+
+
+
+
+
+
104
+
+
+
+
+
+
+
+
105
+ colony confirmed by agglutination – no colonies confirmed by agglutination
Although Method 2 was able to recover E. coli O157:H7 isolates at inoculation levels as
low as 100 CFU/mL, it was only effective for E. coli O157:H7 recovery at an inoculation of 104
CFU/mL, but not 105 CFU/mL. This limited effectiveness occurred because in one sample
(sample 3) Method 2 only recovered E. coli O157:H7 at an inoculation level of 104 CFU/mL. As
a comparison, Method 3 recovered E. coli O157:H7 from the same surface water sample at the
101 CFU/mL and each of the higher inoculation levels.
Method 3 was the most effective method for recovering E. coli O157:H7, detecting
confirmed isolates at inoculation levels as low as 100 CFU/mL, and effectively isolating E. coli
O157:H7 from all three samples at 102 CFU/mL. The detection of E. coli O157:H7 using Method
3, did not appear to be affected by the level of background microflora in the sample because in
the sample with the lowest level of background microflora (sample 1), E. coli O157:H7 was
recovered at the highest inoculation level (102 CFU/mL) of the three samples.
In order to obtain pure E. coli O157:H7 cultures, sometimes suspect colonies needed to
be streaked again onto SMACCT agar before streaking onto E. coli O157:H7 Chromogenic agar.
89 4.5 DISCUSSION AND CONCLUSIONS
For both E. coli O157:H7 and Salmonella, the two enrichment methods were more
effective at recovering pathogens from surface water samples than the membrane filtration
method. Using one of these new methods will help ensure that the 2011 survey will identify any
low levels of pathogens in water samples.
For both pathogens, multiple re-streakings were often required to obtain pure isolates. As
such, extra plates should be prepared. For both pathogens, agglutination results were often
inconclusive indicating that this may be an imprecise technique for pathogen confirmation in
water samples. Therefore, in order to limit the impact that human error or inconclusive
agglutination could have on the results, it is recommended that a polymerase chain reaction
(PCR) step should be used instead of agglutination for final confirmation of isolates.
For Salmonella, Method 2 had an improved recovery rate at lower inoculation levels
compared to Method 3. This may have been a result of the difference in incubation temperature
between the two broths. The higher incubation temperature used in Method 2 may have
preferentially selected for Salmonella by inhibiting the growth of background microflora that do
not grow well at higher temperatures. Furthermore, the observation that more Salmonella isolates
were found when the sample was first plated onto Brilliant Green agar and subsequently streaked
on XLD agar than when plated in the opposite order, could be because BG agar suppressed
background microflora growth more effectively than XLD agar. However, because some
confirmed isolates were only obtained using one particular plating order, it is recommended that
both plating orders should be used. For these reasons, Method 2 is the preferred method for
Salmonella detection and was used for the 2011 survey.
90 The E. coli O157:H7 methods were less effective at recovering the target organism than
were the Salmonella methods. This is to be expected as E. coli O157:H7 is known to be a
difficult microorganism to recover from water (Mull and Hill 2009). In order to counteract this,
some researchers have used immunomagnetic separation methods (Mull and Hill 2009);
however, due to the cost of the required equipment this was not feasible in this study. In this
experiment, samples were held in the broth for 24 hours, in contrast to the normal 6 hour
incubation for EHEC and ECNovo broths, in order to allow as much E. coli O157:H7 to grow as
possible to compensate for not using immunomagnetic separation.
Of the E. coli O157:H7 detection methods studied, Method 3 was the most effective at
recovering pathogens at low inoculation levels. Method 2 was only able to recover E. coli
O157:H7 from sample 3 at an inoculation level of 104 CFU/mL. This sample had the highest
background microflora, indicating that this method may be affected by microflora levels or by a
particular organism or particle in the water sample. Furthermore, similar to the Salmonella
methods, Method 3, which was incubated at the higher temperature, was more effective in
pathogen recovery than the method which incubated broth at 37°C. For these reasons, Method 3
is the preferred method for E. coli O157:H7 detection and was used in the 2011 survey.
91 CHAPTER 5. TEMPERATURE ANALYSIS OF WATER SAMPLES MAILED DURING
SUMMER GROWING SEASON
5.1 ABSTRACT
In order to perform research on the effects of mailing on a surface water sample (Chapter
6), it is necessary to determine temperature extremes that might be encountered during mailing.
This chapter will investigate the maximum temperatures which samples can reach during
overnight mailing during the hot summer months. Water samples (250mL) were collected in
Wyomissing, Pennsylvania at three points during the summer. A temperature data logger was
added into the water sample and samples were packaged and mailed via USPS to University
Park, Pennsylvania. All samples reached equilibrium with the ambient temperature within a 30
hour time frame. A single flexible gel coolant blanket and basic cardboard box provided
insufficient cooling to affect the maximum temperature reached. The maximum temperature
reached among all sample was 39°C.
5.2 INTRODUCTION
In order to investigate the effects of time and temperature on surface water during
mailing (Chapter 6), the temperature extremes which water samples may encounter during
mailing need to be identified. This study will study the maximum temperatures reached by water
samples during overnight mailing in the hot summer months.
5.3 MATERIALS AND METHODS
Water testing kits consisting of a 250 mL HDPE water sampling bottle (2¼x4”), bubble
wrap, and mailing box (4¼x4¼x10½”) were obtained from the Penn State Agricultural
Analytical Laboratory (University Park, PA, USA). Water was collected at the Tulplehocken
92 River in Wyomissing, PA. To each filled bottle, a temperature data logger (HOBO Pendant
Temperature Data Logger, Onset, Pocasset, MA, USA) was added. One water sample was
wrapped in plastic bubble wrap and placed in the mailing box, while the second water sample
was wrapped in a flexible gel ice blanket (8½x11”; Cryopak, Edison, NJ, USA) at -18°C,
covered in bubble wrap and placed into a mailing box. Both boxes were sealed with mailing tape
and sent via United States Postal Service express mail to the Food Science Building at The
Pennsylvania State University, University Park, PA. Data loggers were retrieved from the
samples after 30 hours and data offloaded using HOBOware Lite Software (Onset, Pocasset,
MA, USA). Graphs of temperature data were created using Microsoft Excel 2007 (Microsoft
Corporation, Redmond, WA, USA). This study was repeated three times throughout the 2011
survey period, on June 28, July 21, and August 2.
Temperature data for Wyomissing, PA and State College, PA was accessed from Weather
Underground (www.wunderground.com) as described in Chapter 3.
5.4 RESULTS
The daily air temperatures experienced on the day of mailing and the following day are
displayed in Table 5.1. One of the mailings occurred during the hottest two-day span of summer
2011, July 21 and 22.
TABLE 5.1. Daily high and low temperatures in Wyomissing and State College, PA during
overnight mailing studies.
Wyomissing, PA
State College, PA
High (°C) Low (°C) High (°C) Low (°C)
Mailing
Date
28°C
20°C
N/A
June 28th, 2011
1
th
N/A
24°C
15°C
June 29 , 2011
39°C
25°C
N/A
July 21st, 2011
2
N/A
39°C
26°C
July 22nd, 2011
36°C
20°C
N/A
August 1nd, 2011
3
N/A
31°C
18°C
August 2rd, 2011
93 Figures 5.1, 5.2, and 5.3 show the temperature profiles of the two water samples mailed
in June 28th, July 21st, and August 1st, respectively. In each figure, graph a represents the water
sample mailed without coolant, and graph b represents the sample mailed with a flexible ice
blanket.
a
b
FIGURE 5.1. Temperature profile of water samples mailed from Wyomissing via USPS Express
Post to University Park on June 28-29, 2011 with coolant (a) and with a flexible gel ice sheet (b).
a
b
FIGURE 5.2. Temperature profile of water samples mailed from Wyomissing via USPS Express
Post to University Park on July 21-22, 2011 with coolant (a) and with a flexible gel ice sheet (b).
a
b
FIGURE 5.3. Temperature profile of water samples mailed from Wyomissing via USPS Express
Post to University Park on August 1-2, 2011 with coolant (a) and with a flexible gel ice sheet (b).
94 Samples mailed in June reached maximum temperatures of 26.0 and 25.9°C for samples
mailed without and with coolant, respectively. In July, samples reached 39.20°C when packed
without ice and 39.76°C when packed with coolant. Finally, samples mailed in August reached
31.68 and 31.47°C, respectively, for samples mailed without and with coolant.
These daily temperatures affected the maximum temperature each sample reached during
mailing. All six water samples mailed in this study reached temperatures equivalent to the daily
outside temperature maximums in the city of package reception on the day after mailing. The
absolute maximum temperature reached in all samples was 39°C.
The samples wrapped in a flexible gel ice blanket were rapidly cooled initially by the gel
ice blanket to a lower temperature than the non-temperature controlled samples; however, these
ice blankets did not provide enough cooling to maintain this low temperature, and therefore did
not affect the maximum temperature which was reached by the samples.
5.5 DISCUSSION AND CONCLUSIONS
Samples mailed overnight will reach equilibrium with the external temperature of the
location where they are stored, either the indoor room temperature or the outdoor temperature
during transportation. In this study the maximum temperatures reached were approximately
equivalent to the maximum air temperature encountered during shipping from Wyomissing to
State College. This may occur when a sample sits for long periods of time on a loading dock or
on a delivery truck. Therefore, for the Penn State Agricultural Analytical Laboratory water
testing kit, a single flexible ice blanket is insufficient to control the temperature of a water
sample after a 30 hour period. If water samples require temperature control, methods other than
an ice blanket need to be investigated.
95 CHAPTER 6: EFFECTS OF SAMPLE HOLDING TIME AND TEMPERATURE ON
MICROBIOLOGICAL POPULATIONS IN SURFACE WATER SAMPLES
6.1. ABSTRACT
Standard methods for water analysis dictate that surface water samples for
microbiological testing must be analyzed within 6 hours of collection. It is permissible to hold
potable or drinking water for periods of up to 30 hours before sampling. This study examines the
effects of different holding times and temperatures on the microbiology of surface water
samples. Agricultural surface water samples were collected and tested for five microbial
indicators, total plate count, coliform, fecal coliform, E. coli, and enterococci. Initial microbial
analysis was performed within one hour of sample collection. Samples were divided into five
equal amounts and were held at 4°C, 10°C, 21°C, 30°C, and 39°C. Subsequent analysis for
indicator organisms was performed after 6, 18, and 30 hours. There were no significant
differences (p>0.05) between mean indicator organism levels after 30 hours if samples were held
at 4°C or 10°C, with the exception of enterococci. Samples held at the three higher temperatures
did show significant differences in levels of some indicator organisms (p<0.05). The holding
time for surface water samples could be increased to 30 hours if samples are continuously held at
or below 10°C.
6.2. INTRODUCTION
Current American Public Health Association standard methods for water testing dictate
that surface water to be analyzed for microorganisms must be tested within six hours of sample
collection (APHA 2005). In contrast, well and municipal water can be held up to 30 hours prior
96 to testing. The shorter time limit for surface water is based on the premise that higher levels of
nutrients in a pond, river, or creek water are more supportive of rapid microbial growth. This
requirement may be unachievable for growers in remote locations where approved water testing
facilities are not within a reasonable driving distance. The Pennsylvania State University
Agricultural Analytical Laboratory (University Park, PA) currently offers a mail-in well water
testing kit for growers. Samples can be collected by the grower and mailed overnight to the
laboratory for next day analysis. If it could be shown that, under certain conditions, no changes
in surface water microbial levels occur for up to 30 hours of holding, the well water testing kit
could be adapted for growers who wish to mail surface water samples. This mail-in kit could be
used to send water samples to any water testing laboratory across the state. This chapter
examines the effects of different holding times and temperatures on the microbiology of surface
water samples.
6.3. MATERIALS AND METHODS
6.3.1. Collection and treatment of surface water samples
Surface water samples were collected from five sources in Centre County, Pennsylvania.
Two samples were collected from the Penn State analytical research farm at Rock Springs, one
from Shavers Creek Penn State environmental research facility, and two from two commercial
farms in Julian and Centre Hall, PA. One-liter grab samples were collected in sterilized HDPE
bottles (Ben Meadows, Janesville, WI, USA) using the modified sampling pole (Whirlpak, Fort
Atkinson, WI, USA) described in Chapter 3. Samples were transported at 4°C in a portable
electric cooler (Koolatron, Brantford, ON, Canada) to the laboratory for analysis. Each sample
was equally divided into five sterilized 250-mL glass bottles (VWR, Radnor, PA, USA) and held
97 in a water bath at 4, 10, 21, 30, and 39°C (the maximum temperature reached during the mailing
study, Chapter 5). Care was taken to ensure that the water level in the water bath was at least two
inches above the water sample, to ensure homogeneous heating or cooling.
6.3.2. Microbial analysis
Microbial analysis of samples was performed in the Penn State Food Science Department
microbiological laboratory within one hour of sample collection. This initial microbial analysis
was considered time 0. Microbial analyses were subsequently performed 6, 18, and 30 hours
(time 6, 18, 30) thereafter. Before each analysis was conducted, the water sample was shaken
and tested within 15 minutes as described in the Bacteriological Analytical Manual (FDA
2002b). Ten milliliter sequential dilutions were made by adding 1 mL of the sample to 9 mL of
Buffered Peptone Water (BPW; Becton Dickinson and Company (BD), Sparks, MD, USA). TenmL of each dilution, representing 101 CFU/m/l through 10-5 CFU/mL, were filtered through a
47mm cellulose membrane with a pore size of 0.45µm (PALL, Port Washington, NY, USA)
using a sterilized membrane filtration unit (PALL). After filtration, the membranes were placed
onto different selective agars for each indicator organisms. The plating agars were Modified
Heterotrophic Plate Count agar (mHPC agar; BD) to enumerate the total number of aerobic plate
count in the sample, Modified Endo LES agar (mENDO agar; BD) for simultaneous detection of
coliforms and E. coli, and KF Streptococcus agar (BD) supplemented with 1% TTC solution
(BD) for enumerating enterococci. All three of these agars were incubated at 37°C for 24 hours.
Fecal coliforms were detected by placing filtration membranes on Modified Fecal Coliform agar
(mFC agar; BD) supplemented with 1% Rosalic Acid (Thermo Scientific, Lenexa, KS, USA) and
incubated at 42°C for 24 hours.
98 6.3.3. Analysis of physical characteristics
Water samples were analyzed in the laboratory for pH, conductivity, and turbidity. pH
was determined using a Mettler Toledo SevenEasy pH meter (Mettler Toledo, Columbus, OH,
USA). Conductivity was determined using a YSI Environmental Conductivity Meter (YSI
Incorporated, Yellow Springs, OH, USA). Turbidity was measured using a HACH 2011P
Turbidimeter (HACH Lange, Dusseldorf, Germany).
A portion of the each water sample was reserved and sent to the Pennsylvania State
Agricultural Analytical Laboratory (University Park, PA) for determination of total alkalinity,
bicarbonates, carbonates, residual sodium carbonate, hardness, total dissolved solids, calcium,
magnesium, sodium, sodium adsorption ratio, chloride, boron, nitrate-nitrogen, ammoniumnitrogen, phosphorus, potassium, sulfur, iron, manganese, copper, molybdenum, and zinc.
6.3.4. Statistical Analysis
Statistical analysis was performed using the Statistical Analysis Software (SAS) system
version 9.3 (SAS Institute Inc, Cary, NC, USA). Microbial counts were transformed into log10
counts before statistical analysis was performed in order to normalize the data. Paired t-tests
were performed to determine any significant differences in bacterial levels between time 0 and
times 6, 18, or 30.
Changes in microbial populations were expressed as relative log10 values from time 0 so
that samples containing different initial levels of bacteria could be treated as replicates for
statistical analysis. Analyses of variance (ANOVA) and Tukey’s tests were used to determine if
the log10 change between time 0 and times 6, 18, and 30 were significant at each temperature.
99 6.4 RESULTS
Initial levels of microorganisms and physical characteristics for each of the water samples
collected are shown in Table 6.1. Initial levels of microorganisms ranged from undetectable (< -1
log10 CFU/mL) to 5.56 log10 CFU/mL (Table 6.1). Total plate count widest range of initial
values, from 1.15 log10 CFU/mL to 5.56 log10 CFU/mL. Total plate count, coliform, and E. coli
were found at detectable levels in all five samples. Fecal coliform was below the detection limit
in one sample. Enterococci was only isolated from two of the five water samples collected for
this study. The range of values for conductivity (0.05-0.52), turbidity (2.2-7.5), and pH (7.7-8.3)
experienced in this study was smaller than in the surface water survey (Chapter 3). The pH
values for the five samples are higher (more alkaline) than those observe during the surface
water survey. Conductivity and turbidity values observed for these five samples were all lower
than the mean values for conductivity (2.77) and turbidity (16.62) calculated in the survey
(Chapter 3). There trends may have been because samples were collected from a more limited
area over a shorter period of time than in the survey.
TABLE 6.1. Initial bacterial levels and physical characteristics of surface water samples
collected for the temperature holding study.
Date
4/9/12
4/16/12
4/18/12
Sample
Code
1
2
3
4
5
Initial Bacterial Levels (Log10 CFU/mL)
Total
Plate
Fecal
E.
EnteroCount Coliforms Coliforms coli
cocci
5.56
4.27
< -1
0.11
< -1
4.01
3.92
2.70
0.80
< -1
3.65
3.18
1.39
1.91
0.93
3.38
3.03
0.96
1.62
0.41
1.15
2.04
-0.40
1.58
< -1
Physical Characteristics
Conductivity
0.07
0.05
0.35
0.51
0.52
Turbidity
2.2
7.5
7.3
2.9
3.6
pH
7.9
7.7
8.2
8.2
8.3
Table 6.2 shows the physical and chemical characteristics of the five water samples
collected. Compared to the others, samples 1 and 2 had much lower levels of total alkalinity (27
100 and 30 versus 138, 197, and 217), bicarbonates (33 and 37 versus 169, 240, and 265), hardness
(14 and 21 versus 169, 246, and 268), conductivity (0.05 and 0.07 versus 0.35, 0.51, and 0.52),
total dissolved solids (30 and 43 versus 223, 326, and 335), calcium (4.11, and 6.17 versus 40.24,
65.98, and 70.99), magnesium (0.90 and 1.30, versus 16.59, 19.79, an 22.16), sodium (2.25 and
2.02 versus 11.48, 13.55, and 10.31), chloride (3.10 and 3.40 versus 17.90, 23.40, and 18.90),
and nitrate-nitrogen (<0.50 versus 1.78, 2.57, and 2.93) than the other three samples. Sample 5
was higher in total dissolved solids (335), in particular calcium (70.99), magnesium (22.16), and
nitrate-nitrogen (2.93).
Individual samples displayed high microbial level variability in microbial levels between
analysis times as evidenced by some samples which showed growth while others showed no
growth or decreases in populations. The sample with the most microbial population variability
between sampling times was sample 5. In this sample, both growth and decreases in microbial
levels occurred during holding times. As previously described, sample 5 was higher in total
dissolved solids, and a number of individual nutrients than the other four samples. This suggests
that higher nutrient levels may have affected the growth and survival of the microorganisms in
the sample. Sample 1 was the only sample in which levels of E. coli decreased over the 30 hours
of holding. This decrease was up to 2 logs larger at the highest holding temperatures, than those
held at or below 10°C. In sample 1, which had the highest total plate count (5.56 log10 CFU/mL)
and lowest E. coli count (0.11 log10 CFU/mL) of all five samples, the decrease in E. coli could be
explained by inhibition of E. coli by other background microflora. Because of the variability
between samples, statistical analysis methods were used to determine significant temperature
differences by comparing each sample as a replicate.
101 TABLE 6.2. Results of the analysis of water samples used in the holding study obtained from
the Penn State Agricultural Analytical Laboratory.
Sample 1
pH
Turbidity (NTU)
Total Alkalinity
(mg/L)
Bicarbonates (mg/L)
Carbonates (mg/L)
Residual Sodium
Carbonate (meq/L)
Hardness (mg/L)
Electrical
Conductivity
(mmhos/cm)
Total Dissolved
Solids (mg/L)
Calcium (mg/L)
Magnesium (mg/L)
Sodium (mg/L)
Sodium Adsorption
Ratio
Chloride (mg/L)
Boron (mg/L)
Nitrate-Nitrogen
(mg/L)
Ammonium-Nitrogen
(mg/L)
Phosphorus (mg/L)
Potassium (mg/L)
Sulfur (mg/L)
Iron (mg/L0
Manganese (mg/L)
Copper (mg/L)
Molybdenum (mg/L)
Zinc (mg/L)
Sample 2
Sample 3
Sample 4
Sample 5
7.9
2.2
30
7.7
7.5
27
8.2
7.3
138
8.2
2.9
197
8.3
3.6
217
Normal
Range or
Maximum
6.0 – 9.0
1-100
20 - 1000
37
0
0.2
33
0
0.3
169
0
0.1
240
0
0.1
265
0
0.1
N/A
N/A
N/A
21
0.07
14
0.05
169
0.35
246
0.51
268
0.52
<150
0.1 – 2
43
30
223
326
335
<1000
6.17
1.30
2.25
0.22
4.11
0.90
2.02
0.23
40.24
16.59
11.48
0.38
65.98
19.79
13.55
0.38
70.99
22.16
10.31
0.27
3.10
<0.01
<0.50
3.40
<0.01
<0.50
17.90
<0.01
1.78
23.40
<0.01
2.57
18.90
<0.01
2.93
<100
<50
<300
Lower is
better
<250
<2.0
<3.0
<1.01
<0.99
0.99
1.02
1.02
<1.00
<0.03
1.06
3.72
<0.10
<0.01
<0.01
<0.010
<0.01
<0.03
0.98
2.01
<0.10
<0.01
<0.01
<0.010
<0.01
<0.03
1.53
4.51
<0.10
<0.01
0.02
<0.010
<0.01
<0.03
1.34
7.96
<0.10
<0.01
<0.01
<0.010
<0.01
<0.03
1.18
7.60
<0.10
<0.01
<0.01
<0.010
<0.01
<0.03
1 – 20
2 – 20
<0.10
<0.05
<2.0
<0.01
<2.0
Because each of the five samples had different initial levels of microorganisms, samples
could not be compared to one another. Therefore, changes in relative population levels were
expressed as log10 changes from time 0. By using this method, it was possible to study changes in
microbial levels as a function of time and temperature and not as a function of initial
populations.
102 Log10 change values for total plate count, coliform, fecal coliform, E. coli, and
enterococci were plotted in Figures 6.1, 6.2, 6.3, 6.4, and 6.5 respectively. Total plate count,
coliform, fecal coliform, and E. coli each displayed the expected trend of more rapid microbial
growth at higher temperatures. Enterococci, which was detected only in two of the five water
sources, decreased during holding at all five temperatures tested and this decrease was greater at
the higher temperatures. These reductions could have been due to the enterococci’s low initial
levels and therefore its limited ability to compete with the other indigenous bacteria. The limited
sample size for enterococci-containing water samples may have prevented a significant
difference from being seen.
For all five indicator organisms tested, a less than 0.5 log10 change occurred in samples
held at or below 10°C. At 4 and 10°C, population changes from time 0 were not statistically
significant (p>0.05), with the exception of enterococci. As previously mentioned, the limited
number of samples in which enterococci was recovered may have skewed the statistical analysis
for changes in levels of this microorganism.
The high variability in individual samples caused large standard deviations in log10
change values when samples were treated as replicates. Therefore, significant differences were
often not seen even when graphs seem to show trends.
103 *
*
FIGURE 6.1. Log change in the total plate count of surface water samples held at 5 different
temperatures. * indicate data points which show a significant difference from time 0.
*
*
FIGURE 6.2 Log change in the coliform counts of surface water samples held at 5 different
temperatures. * indicate data points which show a significant difference from time 0.
104 *
*
*
**
*
FIGURE 6.3 Log change in the fecal coliform counts of surface water samples held at 5
different temperatures. * indicate data points which show a significant difference from time 0.
FIGURE 6.4 Log change in the E. coli counts of surface water samples held at 5 different
temperatures. * indicate data points which show a significant difference from time 0.
105 *
*
*
*
*
*
**
FIGURE 6.5 Log change in the Enterococci counts of surface water samples held at 5 different
temperatures. * indicate data points which show a significant difference from time 0.
Paired T-tests were used to determine if significant differences in microbial populations
occurred between time 0 and the other three sampling times. This statistical method, used by
Aulenbach (2010), takes into account the fact that each measurement (t=6, 18, or 30) is
dependent on the level of the initial measurement (time 0) and therefore log10 changes need not
be used. Samples with different initial levels of microorganisms may therefore be compared
together to provide additional statistical power. Table 6.3 shows the average log change from
time 0 after 6, 18, and 30 hours of holding at holding temperature studied. Paired t-tests indicated
that there was no significant difference (p ≤ 0.05) at 6, 18, or 30 hours for any of the bacteria
measured when water samples were held at 4°C or 10°C, with the exception of enterococci,
confirming the trends seen in the log change values. Levels of entercocci were significantly
different after 30 hours of holding at 10°C and were significantly different after 6 hours of
holding at 4°C, but not after 18 or 30 hours. At holding temperatures above 10°C, significant
changes in bacterial levels occurred in both total plate count and fecal coliform.
106 Table 6.4 shows the results of a comparison of indicator organism levels at time 0 and
time 30 with current irrigation water standards as outlined in Chapter 2 (Table 2.2 and Table
2.3). The table shows that all water samples held at 4°C which passed current standards at time
0, would have passed the same standards after 30 hours of holding.
TABLE 6.3. Average log change in microbial levels from time 0 after 6, 18, and 30 hours, as
obtained by paired t-tests.
Microbial
Indicator
Total Plate
Count
Coliform
Fecal
Coliform
E. coli
Enterococci
Temperature
4°C
6
Hold time (hr)
18
-0.29
30
-0.21
+0.31
10°C
-0.33
-0.11
+0.42
22°C
-0.23
+1.26
+0.71 *
30°C
+0.14
+1.88 *
+1.84 *
39°C
-0.54
+2.53 *
+2.52 *
4°C
-0.11
+0.11
+0.13
10°C
+0.25
+0.03
+0.06
22°C
-0.51
+0.98
+0.76
30°C
-0.22
+0.98
+1.43
39°C
-0.56
+1.04
+1.65
4°C
-0.11
+0.07
-0.01
10°C
+0.11
+0.62
+0.61
22°C
+0.06
+0.73 *
+1.12 *
30°C
+0.23
+2.35 *
+3.61 *
39°C
+0.45
+2.97 *
+3.84 *
4°C
-0.08
+0.12
-0.18
10°C
-0.09
+0.01
-0.34
22°C
-0.36
+0.64
-0.01
30°C
-0.69
+1.01
+2.54
39°C
-0.99
+0.76
+2.39
4°C
-0.35 *
-0.14
-0.36
10°C
-0.50
-0.31
-0.36 *
22°C
-0.57 *
-0.48
-0.47
30°C
-0.63 *
+0.41
-0.39
39°C
-0.54
-1.18 *
-0.74
* indicates a significant difference from t = 0 (α = 0.05)
107 TABLE 6.4. Results for water samples used for holding study when microbial levels from time 0
and time 30 were compared to current surface water standards.
E. coli
126CFU/
100mL
E. coli
235CFU/
100mL
Fecal
Coliform
200CFU/
100mL
Fecal
Coliform
1000CFU/
100mL
Enterococci
33CFU/
100mL
Coliform
1000CFU/
100mL
F
F
F
F
F
F
F
F
F
F
F
F
P
P
P
F
F
F
P
P
P
P
F
F
P
P
P
P
P
P
F
F
F
F
F
F
F
F
P
P
F
F
P
P
P
P
F
F
P
P
F
F
F
F
P
P
P
F
F
F
P
P
P
P
P
P
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
P
P
P
P
P
P
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
P
P
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
SAMPLE 1
Time 0
Time 30
4°C
10°C
20°C
30°C
40°C
SAMPLE 2
Time 0
Time 30
4°C
10°C
20°C
30°C
40°C
SAMPLE 3
Time 0
Time 30
4°C
10°C
20°C
30°C
40°C
SAMPLE 4
Time 0
Time 30
4°C
10°C
20°C
30°C
40°C
SAMPLE 5
Time 0
Time 30
4°C
10°C
20°C
30°C
40°C
108 6.5 DISCUSSION AND CONCLUSIONS
These results show that for accurate microbial analysis, surface water should not be held
for extended periods of time at or above room temperature (22°C) since microbial levels may
change significantly and thus alter the results of a water test. On the other hand, results show that
surface water can be held at or below 10°C for up to 30 hours without significant changes
occurring in levels of indicator organisms, with the exception of enterococci. This difference
may have been a result of the limited number of samples collected which contained detectable
levels of enterococci. Interestingly, enterococci is the only bacterial type which showed a trend
of levels decreasing with longer holding times. This decrease could have been due to the lower
initial detection levels of enterococci than the other indicator bacteria and competitive inhibition
by other microflora. Since enterococci is used as an indicator in the Pennsylvania Department of
Environmental Protection water standards, further research needs to be performed before holding
times are extended for water samples to be compared to this standard.
A comparison of the indicator levels to current surface water standards showed that all
samples which would have passed current standards at time 0 would also have passed when
tested at time 30 if held at 4°C and 10°C. Moreover, those water samples which failed current
standards at time 0 would have failed when tested at time 30. Despite the variability between
individual samples, increasing the holding time to 30 hours therefore would not have affected the
water testing results for any of these samples if they were held at or below 10°C.
These trends indicate that the time limit between sample collection and testing could be
increased to 30 hours if samples are held below 10°C. This increased time limit could help make
water testing more accessible for growers in remote areas where there is not a water testing
facility nearby, and could allow samples to be mailed overnight if particular mailing practices
109 can be identified which consistently hold water samples at or below 10°C. A study to determine
if mailing practices can be developed to achieve this temperature restriction will be addressed in
the next chapter (Chapter 7).
This research did not look at how holding temperatures below 4°C would affect microbial
populations in a water sample. It is possible that water samples mailed with ice packs could
reach temperatures below 4°C and it is therefore important that before a mail-in surface water kit
be developed, this research will be performed at holding temperatures below 4°C to ensure that
changes in microbial populations occur.
This study focused on a limited number of surface water samples in order to be able to
study a wide range of temperature, times, and organisms. Research should continue in this area
to definitively show that holding surface water below 10°C for up to 30 hours does not result in
microbial change. A larger number of samples will help prove that the standard methods and
common practice should be changed to allow this extended holding.
110 CHAPTER 7: IDENTIFICATION OF MAILING PRACTICES TO CONTROL
MICROBIAL GROWTH IN SURFACE WATER SAMPLES
7.1 ABSTRACT
Current standard methods dictate that surface water samples be analyzed for
microorganisms within 6 hours of sample collection. Previous research (Chapter 6) has shown
that this holding interval can be increased for up to 30 hours as long as the sample is held below
10°C. This 30 hour time limit would allow for surface water samples to be mailed to a testing
laboratory as long as mailing practices could be identified which would maintain the water
sample at or below 10°C. This study investigated different shipping supplies and practices to
determine if water samples could be kept below 10°C during 30 hour mailing. The most effective
method for maintaining water temperature during mailing was in an 8x6x4¼” Styrofoam mailing
kit with 2 15-oz ice blocks. This method was only able to maintain water samples below 10°C
when the sample box was held at 25°C, and not at the temperature extreme of 39°C. If this
shipping method is to be used for surface water samples, precautions such as labels on the
shipping boxes and temperature history indicator strips should be employed.
7.2. INTRODUCTION
Previous research (Chapter 6) showed that the time limit from surface water sample
collection to microbial testing can be increased to 30 hours if the water sample can be held at or
below 10°C. Increasing this time limit to 30 hours would allow farmers in remote areas to mail
their water sample to a testing facility using overnight delivery, but only if the water could be
maintained at 10°C during the entire course of mailing. In Chapter 5, it was shown that a water
111 sample with no coolant or limited coolant and no insulation will reach temperature equilibrium
with the ambient temperature within 30 hours. This study investigates whether alternative
mailing practices can maintain water samples at an appropriate temperature throughout the 30
hours under the most extreme temperature conditions determined in Chapter 5.
7.3. MATERIALS AND METHODS
Temperature data loggers (HOBO Pendant Temperature Data Logger, Onset, Pocasset, MA,
USA), programmed to take temperature readings every 10 minutes, were placed inside 250-mL
LDPE wide mouth water sampling bottles (2.5x4”: VWR, Radnor, PA, USA). Water samples
were held for 30 hours in various insulated shipping containers and coolant volumes
arrangements. All packaging materials were obtained from Uline (Pleasant Prairie, WI, USA).
Table 7.1 lists the shipping materials, coolant, and storage conditions for each sample. Eight
water samples were held at 39°C, the highest temperature reached in the initial mailing study
(Chapter 5), and eight samples were held at 25°C. After 30 hours, data loggers were retrieved
from the water bottles and the data were downloaded and plotted using Microsoft Excel 2007
(Microsoft Corporation, Redmond, WA, USA).
112 TABLE 7.1. Packing, Coolant, and Storage conditions used for each water sample.
Sample
Holding
Temperature
Pre-cooled
to 10°C
1
39°C
N

2
39°C
N

Packaging

3
39°C
N
4
39°C
N
5
39°C
Y





6
39°C
Y


7
39°C
Y
8
39°C
Y


9
25°C
N




10
25°C
N


11
25°C
N



12
25°C
N
13
25°C
N


Coolant
8X6X4¼” Styrofoam mailing kit
with cardboard box
12x16” insulated mailing envelope

12x17” foil laminated metalized
bubble envelope
8x6x7” Styrofoam mailing kit with
cardboard box
12x17” foil laminated metalized
bubble envelope
12x16” insulated mailing envelope
8x6x4¼” Styrofoam mailing kit
with cardboard box
12x17” foil laminated metalized
bubble envelope
8x6x7” Styrofoam mailing kit with
cardboard box
12x17” foil laminated metalized
bubble envelope
12x16” insulated mailing envelope
11¼x3x151/8” side loader cardboard
mailing box
12x16” insulated mailing envelope
11¼x3x151/8” side loader cardboard
mailing box
8x6x4¼” Styrofoam mailing kit
with cardboard box
12x17” foil laminated metalized
bubble envelope
8x6x7” Styrofoam mailing kit with
cardboard box
12x17” foil laminated metalized
bubble envelope
12x16” insulated mailing envelope
11¼x3x151/8” side loader cardboard
mailing box
12x16” ” insulated mailing
envelope
11¼x3x151/8” side loader cardboard
mailing box
8x6x4¼” Styrofoam mailing kit
with cardboard box


2 15oz ice
bricks
2 24oz ice
packs
2 12oz ice
packs

2 24oz ice
packs

2 15oz ice
blocks
2 12oz ice
blocks


2 12oz ice
packs

2 24oz ice
packs

2 15oz ice
blocks
2 12oz ice
packs


2 12oz ice
packs

2 24oz ice
packs

2 15oz ice
blocks
113 7.4. RESULTS
Figure 7.1 shows the temperature profiles for treatments 1, 2, 3, and 4, each of which
were held at 39°C. The number on each chart corresponds to the packaging and coolant
arrangement listed in Table 7.1. The solid line indicates the target maximum sample temperature
of 10°C. The samples reached maximum temperatures of between 19.5°C and 31.7°C. Therefore,
it was not possible for any of the samples to maintain temperatures at or below 10°C for 30
hours. Only one of these four samples reached a temperature lower than 5°C.
FIGURE 7.1. Temperature profiles of water samples 1, 2, 3, and 4, held at 39°C for 30 hours.
In order to determine if lower sample temperatures could be achieved, the next four water
samples were pre-cooled in the refrigerator for two hours. Figure 7.2 shows the temperature
profiles of the four samples pre-cooled at 4oC for two hours and then held at 39°C for 30 hours.
114 Although pre-cooling allowed these samples to reach lower minimum temperatures, maximum
temperatures still rose to between 17.7°C and 34.9°Cm which is above the target temperature.
FIGURE 7.2. Temperature profiles of water samples 5, 6, 7, and 8, precooled to refrigeration
temperatures and held at 39°C for 30 hours.
Because all the samples tested failed to maintain temperatures at or below 10°C when
held at 39°C, methods which investigated using less extreme temperatures. Figure 7.3 shows the
temperature profiles of four samples held at 25°C for 30 hours. These samples reached maximum
temperatures between 3.37°C and 13.27°C. Three out of the four samples (9, 10, and 12) were
maintained below 10°C. The lowest final temperature was reached by sample 9 (3.37°C), which
utilized the Styrofoam mailing kit and 2 15-oz ice blocks.
115 FIGURE 7.3. Temperature profiles of water samples 9, 10, 11, and 12, held at 25°C for 30
hours.
The most effective method for maintaining temperature, a 8x6x4¼” Styrofoam mailing
kit with two 15-oz ice blocks, was replicated with four additional samples to confirm
effectiveness. Replicate samples, graphed in Figure 7.4, confirm the initial finding.
116 FIGURE 7.4. Temperature profiles of replicate water samples 13 held at 25°C for 30 hours.
7. 5 DISCUSSION AND CONCLUSIONS
The results of this study showed that it was not possible to maintain temperatures at or
below 10°C when sample boxes were held 39°C, even if samples were pre-cooled in the
refrigerator. When samples were held at a temperature slightly above room temperature (25°C),
all but one was maintained below 10°C. The most effective mailing method was a 8x6x4¼”
Styrofoam mailing kit with 2 15-oz ice sheets. This shipping system can therefore be effective at
maintaining water samples at temperatures which will not alter microbial populations, if the
shipment is not subjected to extreme temperatures. Precautions should be taken to prevent this
from occurring, such as affixing a “Keep Package Cold” label on the shipping box, and placing
temperature history indicator strips (3M, St. Paul, MN, USA) on the outside of the water sample
bottle.
117 Before this kit is used in by growers to mail water samples into a laboratory, the kit
should be validated with microbial data. Microbial populations in surface water samples should
be enumerated prior to the sample being placed into the mail-in testing kit. Once the samples
arrive at the lab, the microbial populations should be enumerated again in order to determine if
the kit consistently prevents changes in bacterial levels during mailing.
118 CHAPTER 8. SUMMARY AND CONCLUSIONS
The first objective of this thesis was to determine levels of pathogenic and nonpathogenic bacteria in Pennsylvania surface waters. Results from the microbial survey of 2010
and 2011 in Chapter 3 showed great variation in microbial indicator population; from < 0 log10
CFU/mL to > 4.5 log10 CFU/mL. Levels of indicator bacteria also varied within individual
sampling locations, indicating that the time or day of sampling may affect the results of a water
test, and thus prevent a water test from providing a true representation of the ongoing microbial
levels of a water source. If rolling geometric means are utilized in water sampling, as required in
EPA Recreational water standard, this effect may be decreased along with the failure rate for
water samples.
No pathogens were found in 2010 using a microbial enumeration method. Enrichment
methods were developed for the 2011 study (Chapter 4). The results for 2011 in Chapter 3 show
that Salmonella spp. was isolated from five water samples at four sites during the 2011 survey.
Three of the Salmonella-positive samples were collected at locations where Salmonella spp. had
not been recovered at the other two sampling times. E. coli O157:H7 was not isolated from any
of the water samples. Salmonella spp. was found in the second year at rates slightly lower than in
other studies. This may have been because, in contrast to other studies, this was a broad survey
and sites known to be highly contamination were not intentionally selected.
Results for the second objective was to identify correlations between indicator organisms,
physical characteristics, and pathogens. Due to the low level of pathogens found in the water
samples, correlations could not be identified between pathogens and indicators or physical
characteristics. All indicator organisms analyzed showed significant correlations to one another.
A significant correlation was seen between pH and each of the indicator organisms. Significant
119 correlations were found between some indicator organisms and conductivity and turbidity;
however neither of these showed significant correlation with all indicator organisms. No
significant correlations were found between precipitation and any indicator organisms.
The third objective was to investigate the ability of the current surface irrigation water
standards to predict microbiological safety. Because large numbers of water samples failed to
meet each of the current standards yet no pathogens were found, the results suggested that the
current guidelines are too restrictive and would eliminate the use of many safe water sources.
However, four out of the five Salmonella spp.-positive water samples passed at least one current
surface irrigation water standard. This conversely indicates that, in some cases, the current
standards are not strict enough to identify unsafe water sources. Thus, surface water standards
were shown to be both too strict and too lenient, and therefore may not be effective at identifying
unsafe surface water sources.
The inability of current surface water standards to predict the safety of a water source
may indicate that these standards shouldn’t be used. If they are not used, perhaps water samples
should be taken after microbial interventions are used by the grower, such as the addition of
disinfectants or filtration, so that results will accurately represent the water which will contact
the crop. This will reduce the level of all bacteria in the water sample, and help growers meet
current standards. Growers should be encouraged to use these interventions in their operations to
improve public safety, because methods to limit the level of indicator organisms in a sample will
likely also reduce or eliminate pathogens if present.
The only way to effectively predict pathogen presence in a water source is to test directly
for human pathogens, instead of indicator organisms. Salmonella and E. coli O157:H7 detection
in 2010 was hindered by analysis methods with high detection limits, and an agglutination
120 confirmation method which was subject to human error. If pathogen testing is to replace
indicator organism testing for determining water safety, it is important that pathogen methods be
refined to allow for faster, automated, inexpensive methods which are less subject to human
error. Until more appropriate pathogen detection methods are developed and made widely
available, E. coli standards should be used to assess water quality because this species is the only
true indicator of fecal contamination.
Using physical characteristics to predict microbial levels may not be possible as there are
too many uncontrolled variables to measure and the way that each of these physical
characteristics interact with each other and the water source are not well understood.
The fourth objective was to determine how holding time and temperature will affect the
microbial populations in a surface water sample. In Chapter 5, it was determined that mailing a
water sample in the Penn State Agricultural Analytical laboratory mailing kit with or without
coolant will reach ambient temperature of up to 39°C within 30 hours of mailing. The study in
Chapter 6 showed that at temperatures between 20°C and 39°C, total plate count and fecal
coliform populations changed significantly after 30 hours of holding; however populations of all
indicator organisms did not significantly change within 30 hours in the surface water samples
held at 4°C or 10°C.
The fifth objective was to identify mailing practices which will maintain a water sample
at or below a temperature which prevents microbial growth in the sample. In Chapter 7, the most
effective method tested to control water temperature during mailing was by using a 6x8x4¼”
Styrofoam mailing kit and 2 15-oz ice blocks. However, this method was only effective when the
shipment was subjected to 25°C conditions, but not 39°C. Therefore additional precautions
should to be taken during mailing to ensure that the water sample does not exceed 10°C.
121 It is recommended that an 8x6x4¼” Styrofoam mailing kit containing 2 15oz ice bricks
should be used and labeled “Keep Package Cold” and that a temperature history indicator strip
be placed on the outside of the bottle. The kit should contain written directions for growers on
sample collection and handling practices, along with a sample submission form directing the
laboratory to test for E. coli levels since this bacterial species remains the only true indicator of
fecal contamination.
122 CHAPTER 9. FUTURE RESEARCH
Further research should be conducted on the relationship between indicator organisms
and human pathogens. This research could be performed on a limited number of sites with more
frequent observations over muti-years to create more power for determining significant trends.
Additionally, research which investigates this relationship after any grower interventions (such
as chlorination or filtration) may help to understand if current standards would be effective if
water samples were taken from this location.
Alternatively, the use of indicator organisms as a marker of water safety could be rejected
since they do not seem to effectively correlate to water safety, and water testing should be
performed for pathogens directly. Currently, the cost, time, and expertise required to test surface
water for pathogens makes this type of testing prohibitive for most water testing laboratories and
growers. Further research needs to be done to develop faster, inexpensive, automated methods
for direct pathogen testing.
The correlation observed in the surface water survey between indicator organisms and the
pH of the water source should be further investigated to determine if this relationship holds true
over a wider variety of water sources. If this relationship proves valid, research should
investigate if this correlation can be exploited to help predict higher risk water sources for further
testing, or if pH interventions can help reduce microbial load. This survey found no correlation
between precipitation and indicator organism level, a correlation found in other surveys. Further
research should be performed which directly investigates this relationship at one sampling
location with frequent observations throughout an entire growing season to capture a greater
range of precipitation levels, because this relationship could have an effect on the timing of
surface water sampling.
123 Further research should be performed on the microbiological effects of holding surface
water samples for 30 hours with a larger number of water samples. A very limited number of
samples was used in this research and in order for standard methods to change it needs to be
proven that the lack of microbial change in samples held at or below 10°C holds true across a
wide variety and large number of samples. Additionally, the study of mailing practices showed
that some mailing conditions could hold water samples below 4°C. Because of this, the
microbiological effects of holding surface water samples for 30 hours at temperatures between 0
and 4°C should be investigated.
Finally, before a mail-in surface water testing kit can be used by farmers it needs to be
validated using microbial studies. In order to ensure that a mail in surface water testing kit
controls microbial growth and decay during transport, microbial populations of water samples
should be enumerated before and after overnight mailing using the recommended mail-in surface
water testing kit.
124 REFERENCES
3M. 2012. Indicator Organism Testing. Available online at: solutions.3m.com/wps/portal/3M
/en_US/Microbiology/FoodSafety/produc-tinformation/product-catalog/?PC_7_RJH9U523003
DC023S7P92O3O87000000_nid=2BJ86690LFbe29BDXSBJ7Fgl
Abdul-Raouf U. M., L. R. Beuchat, and M. S. Ammar. 1993. Survival and Growth of
Escherichia coli O157:H7 in Ground, Roasted Beef as Affected by pH, Acidulants, and
Temperature. Appl. Environ. Microbiol. 59:2364–2368.
Ackers M. L., B. E. Mahon, E. Leahy, B. Goode, T. Damrow, P. S. Hayes, W. F. Bibb, D. H.
Rice, T. J. Barrett, L. Hutwagner, P. M. Griffen, and L. Slutsker. 1998. An Outbreak of
Escherichia coli O157:H7 Infections Associated with Leaf Lettuce Consumption. J. Infect. Dis.
177:1588-1593.
Ait Melloul A., and L. Hassani. 1999. Salmonella Infection in Children from the WastewaterSpreading Zone of Marrakesh City (Morocco). J. Appl. Microbiol.87:536-539.
Alsanius B. W., A. K. Gustafsson, and M. Hultberg. 2010. Microbiological Aspects on Irrigation
Water Quality to Field Grown Vegetables. Acta. Hort. (ISHS) 852:53-60.
AMI and PSU [American Mushroom Institute and Pennsylvania State University]. 2008.
Mushroom Good Agricultural Practices Program. Available online at: http://www.wga.com/
DocumentLibrary/MGAP_v_15_May_2010.pdf
Andersson Y., and P. Bohan. 2001. Disease Surveillance and Waterborne Outbreaks. IWA
Publishing:WHO. Available online at: http://www.who.int/water_sanitation_health/dwq/
iwachap6.pdf
th
AOAC. 2000a. Method 991.14. In Official Methods of Analysis of AOAC International. 17 ed. pp. 2223. AOAC, Gaithersburg, MD.
th
AOAC. 2000b. Method 998.08. In Official Methods of Analysis of AOAC International. 17 ed.
pp. 39-40 AOAC, Gaithersburg, MD.
APHA [American Public Health Association]. 2001a. Enterobacteriaceae, Coliforms, and
Escherichia coli as Quality and Safety Indicators. In Compendium of Methods for the
Microbiological Examination of Foods. pp. 69-86, American Public Health Association,
Washington DC.
125 APHA [American Public Health Association]. 1928. Microbiological Examination. In Standard
Methods of Examination of Water and Wastewater. American Public Health Association,
Washington DC.
APHA [American Public Health Association]. 2001b. Pathogenic Escherichia coli, Listeria,
Salmonella, and Shigella. In Compendium of Methods for the Microbiological Examination of
Foods. pp. 331-385, American Public Health Association, Washington DC.
APHA [American Public Health Association]. 2005. Microbiological Examination. In Standard
Methods of Examination of Water and Wastewater. pp. 9-1 – 9-140, American Public Health
Association, Washington DC.
Armstrong G. L., J. Hollingsworth, and J. G. Morris. 1996. Emerging Foodborne
Pathogens: Escherichia coli O157:H7 as a Model of Entry of a New Pathogen Into the Food
Supply of the Developed World. Epidemiol Rev 18:29–51.
Arvanitidou M., G. A. Stathopoulos, T. C. Constantinidis, V. Katsouyannopoulos. 1995a. The
Occurrence of Salmonella, Campylobacter and Yersinia spp. in River and Lake Waters.
Microbiol. Res. 150:153-158.
Arvanitidou M., T. C. Constantinidis, V. Katsouyannopoulos. 1995b. A Survey on
Campylobacter and Yersinia spp. Occurrence in Sea and River Waters in Northern Greece. Sci.
Total Environ. 171:101-106.
Ashbolt, N. J. 2004. Microbial Contamination of Drinking Water and Disease Outcomes in
Developing Regions. Toxicology 198:229–238.
Aulenbach B. 2010. Bacteria Holding Times for Fecal Coliform by mFC Agar Method and Total
Coliform and Escherichia coli by Colilert-18 Quanti-Tray Method. Environ. Monit. Assess.
161:147-159.
Bates R. L. and J. A. Jackson. 1987. Glossary of Geology (Third Edition). American Geological
Institute, Alexandria, VA, USA.
Baudart J., J. Grabulos, J. P. Barusseau, and P. Lebaron. 2000. Salmonella spp. and Fecal
Coliform Loads in Coastal Waters from a Point vs. Nonpoint Source of Pollution. J. Environ
Qual. 29: 241-250.
126 Beloti V., J. A. de Souza, M. A. Barros, L. A. Nero, M. R. de Mattos, V. V. Gusmao, and L. B.
de Moraes. 2003. Evaluation of Petrifilm™ EC and HS for Total Coliforms and Escherichia coli
Enumeration in Water. Braz. J. Microbiol. 34:301-304.
Berger C. N., S. V. Sodha, R. K. Shaw, P. M. Griffen, D. Pink, P. Hand, and G. Frankel. 2010.
Fresh Fruit and Vegetables as Vehicles for the Transmission of Human Pathogens. Environ.
Microbiol. 12(9): 2385-2397.
Beuchat L. R. and J. H. Ryu. 1997. Produce Handling and Processing Practices. Emerg. Infect.
Dis. 3(4):459-465.
Beuchat L. R. 2002. Ecological Factors Influencing Survival and Growth of Human Pathogens
on Raw Fruits and Vegetables. Microbes Infect. 4:413-423
Bihn E. A., and S. Reiners. 2011. Good Agricultural Practices and Good Manufacturing
Practices for Vegetable Production. In Handbook of Vegetables and Vegetable Processing. pp.
461-481, Blackwell Publishing Ltd., Ames, Iowa, USA.
Blaser, J. M., and L. S. Newman. 1982. A review of human salmonellosis: I. Infective dose. Rev.
Infect. Dis. 4:1096–1106.
Boase J., S. Lipsky, P. Simani, S. Smith, C. Skilton, and S. Greenman. 1999. Outbreak of
Salmonella Serotype Muenchen Infections Associated with Unpasteurized Orange Juice—United
States and Canada. Morb. Mortal. Wkly. Rep. 48:582–585.
Bonadonna, L., R. Briancesco, M. Ottaviani, and E. Veschetti. 2002. Occurrence of
Cryptosporidium oocysts in sewage effluents and correlation with microbial, chemical, and
physical water variables. Environ. Monit. Assess. 75:241-252.
Brandl M. T., and R. Amundson. 2008. Leaf age as a risk factor in contamination of lettuce with
Escherichia coli O157:H7 and Salmonella enterica. Appl. Environ. Microbiol.74(8):2298-2306.
Brunkard J. M., E. Ailes, V. A. Roberts, V. Hill, E. D. Hilborn, G. F. Craun, A. Rajasingham, A.
Kahler, L. Garrison, L. Hick, J. Carpenter, T. J. Wade, M. J. Beach, and J. S. Yoder. 2011.
Surveillance for Waterborne Disease Outbreaks Associated with Drinking Water – United States,
2007-2008. Morb. Mortal. Wkly. Rep. 60(ss12): 38-68.
Buzby J. C., L. J. Unnevehr, and D. Roberts. 2008. Food Safety and Imports: an analysis of FDA
food-related import refusal reports, Bulletin no. 39. United States Department of AgricultureEconomic Research Service Available online at: http://www.ers.usda.gov/publications/eib39/
127 eib39.pdf
Byappanahalli, M., D. Shively, M. Nevers, M. Sadowsky, and R. Whitman. 2003a. Growth and
Survival of Escherichia coli and Enterococci Populations in the Macro-Alga Cladophora
(Chlorophya). FEMS Microbiol. Ecol. 46:203-211.
Byappanahalli, M., M. Fowler, D. Shively, and R. Whitman. 2003b.Ubiquity and persistence of
Escherichia coli in a midwestern coastal stream. Appl. Environ. Microbiol. 69:4549-4555.
Caldwell E. L., and L. W. Parr. 1935. Present Status of Handling Water Samples. Am. J. Public
Health 23(5):467-472.
California Strawberry Commission. 2005. California Strawberry Commission Food Safety
Program. Available online at: http://www.calstrawberry.com/fileData/docs/FSP_English.pdf
CCME [Canadian Council of Ministers of the Environment]. 1987. Water Quality Guidelines
for the Protection of Agriculture. Available online at: http://ceqg-rcqe.ccme.ca/
Caplenas N. R., and M. S. Kanarek. 1984. Thermotolerant Non-Fecal Source Klebsiella
pneumonia: Validity of the Fecal Coliform Test in Recreational Water. Am. J. Public Health
74(11):1273-1275.
Carter A. M., R. E. Pacha, G. W. Clark, and E. A. Williams. Seasonal Occurrence of
Campylobacter spp. in Surface Waters and their Correlation with Standard Indicator Bacteria.
Appl. Environ. Microbiol. 53(3): 523-526.
CDC [Centers for Disease Control and Prevention]. 2006. On Going Multistate Outbreak
of Escherichia coli Serotype O157:H7 Infections Associated with Consumption of Fresh
Spinach-United States, September 2006. Morb. Mortal. Wkly. Rep. 55:1045–1046.
CDC [Centers for Disease Control and Prevention]. 2008. Outbreak of Salmonella Serotype
Saintpaul Infections Associated with Multiple Raw Produce Items --- United States, 2008. Morb.
Mortal. Wkly. Rep. 57(34): 929-934.
CDC [Centers for Disease Control and Prevention]. 2011. CDC Estimates of Foodborne Illness
in the United States. Available online at: http://www.cdc.gov/foodborneburden/cdc-and-foodsafety.html
Chambers R. M., H. Aird, and F. J. Bolton. 2002. Waterborne Escherichia coli O157. J. Appl.
Microbiol., Symposium Supplement 88:124S-32S.
128 Chandran, A., S. Varghese, E. Kandeler, A. Thomas, M. Hatha, and A. Mazumder. 2011. An
Assessment of Potential Public Health Risk Associated with the Extended Survival of Indicator
and Pathogenic Bacteria in Freshwater Lake Sediments. Int. J. Hyg. Environ. Health 214(3):258264.
Chao W., R. Ding, and R. Chen. 1987. Survival of Pathogenic Bacteria in Environmental
Microcosms. Chin. J. Microb. Immunol. 20:339–348.
Cheong S., C. Lee, S. W. Song, W. C. Choi, C. H. Lee, and S. J. Kim. 2009. Enteric Viruses in
Raw Vegetable and Goundwater Used for Irrigation in Korea. Appl. Environ. Microbiol. 75(24):
7745-7751.
Cooley, M., D. Carychao, L. Crawford-Miksza, M. T. Jay, C. Myers, C. Rose, C. Keys, J. Farrar,
and R. E. Mandrell. 2007. Incidence and Tracking of Escherichia coli O157:H7 in a Major
Produce Production Region in California. PLoS ONE 2(11):e1159.
Cooley M., W. G. Miller, and R. E. Mandrell. 2003. Colonization of Arabidopsis thaliana with
Salmonella enterica and enterohemorrhagic Escherichia coli O157:H7 and competition by
Enterobacter asburiae. Appl. Environ. Microbiol. 69(8):4915–4926.
Craun G. F., J. M. Brunkard, J. S. Yoder, V. A. Roberts, J. Carpenter, T. Wade, R. L. Calderon,
J. M. Roberts, M. J. Beach, and S. L. Roy. 2010. Causes of Outbreaks Associated with Drinking
Water in the United States from 1971 to 2006. Clin. Microbiol. Rev. 23(3): 507-528.
Creel, R. 1912. Vegetables as a Possible Factor in the Dissemination of Typhoid Fever. Public
Health Reports 27:87-93.
CSPI [Center for Science in the Public Interest]. 2008. CSPI Outbreak Alert Data: Info on
Produce Outbreaks. Available online at: http://cspinet.org/new/pdf/cspi_outbreak_alert.pdf
Curiale M. S., T. Sons, I. D. McIver, J. McAllister, B. Halsey, D. Roblee, and T. Fox 1991. Dry
Rehydratable Film for Enumeration of Total Coliforms and Escherichia coli in Foods. J. Assoc.
Off. Anal. Chem. 74:635–648.
Czajkowska D., A. Witkowska-Gwiazdowska, I. Sikorska, H. Boszczyk-Maleszak, and M.
Horoch. 2005. Survival of Escherichia coli serotype O157:H7 in water and in bottom-shore
sediments. Pol. J. Environ. Stud. 14:423–430.
129 Danyluk M. D., M. Nozawa-Inoue, K. R. Hristova, K. M. Scow, B. Lampinen, and L. J. Harris.
2008. Survival and Growth of Salmonella Enteritidis PT 30 in Almond Orchard Soils. J. Appl.
Microbiol. 104:1391-1399.
Danyluk M. D., and D. W. Schaffner. 2011. Quantitative Assessment of the Microbial Risk of
Leafy Greens from Farm to Consumption: Preliminary Framework, Data, and Risk Estimates. J.
Food Protect. 74(5):700-708.
Debroy C. E., E. Roberts, A. Valadez, E. G. Dudley, and C. N. Cutter. Detection of Shiga ToxinProducing Escherichia coli O26, O45, O103, O111, O113, O121, O145, and O157 Serogroups
by Multiplex Polymerase Chain Reaction of the wzx Gene of the O-antigen Gene Cluster.
Foodbrne. Path. And Dis. 8:651-652.
Deering, A. J., R. E. Pruitt, L. J. Mauer, B. L. Reuhs. 2011. Examination of the internalization of
Salmonella serovar Typhimurium in peanut, Arachis hypogaea, using immunocytochemical
techniques. Food Res. Int. 45(2):1037-1043.
Deering A. J., L. J. Mauer, and R. E. Pruitt. 2012. Internalization of E. coli O157:H7 and
Salmonella spp. in Plants: A Review. Food Res. Int. 45:567-575.
Doyle M. P. and L. R. Beuchat. 2007. Foodborne Pathogenic Bacteria. In Food Microbiology:
Fundamentals and Frontiers. American Society of Microbiology Press, Washington, DC.
Doyle M. P. and M. C. Erickson. 2008. Summer Meeting 2007 – The Problems with Fresh
Produce: An Overview. J. Appl. Microbiol. 105(2): 317-320.
Dutka B. J., and A. El-Shaarawi. 1980. Microbiological Water and Eluent Sample Preservation.
Can. J. Microbiol. 26(8):921-929.
D’Souza R. M., N. G. Becker, G. Hall, and K. B. Moodie. 2004. Does Ambient Temperature
Affect Foodborne Disease? Epidemiology 15:86–92.
EPA [Environmental Protection Agency]. 2003. Reports and References - Bacterial Water
Quality Standards for Recreational Waters (Freshwater and Marine Waters). Available online at:
http://water.epa.gov/type/oceb/beaches/local_index.cfm
EPA [Environmental Protection Agency]. 1984. Health Effects Criteria for Fresh Recreational
Water. Available online at: http://www.epa.gov/microbes/documents/frc.pdf
130 EPA [Environmental Protection Agency]. 2010. Watershed Assessment, Tracking, and
Environmental Results. Available online at:
http://iaspub.epa.gov/waters10/attains_index.control?p_area=VA.
EPINET. 2011. Overview of Indicator Organisms. Available online at:
http://www.epi-net.org/eng/Overview_of_Indicator_Organisms.pdf
Erickson M. C., C. C. Webb, J. C Diaz-Perez, S. C. Phatak, J. J. Silvoy, L. Davey, A. S. Payton, J. Liao,
L. Ma, and M. P. Doyle. 2010. Surface and Internalized Escherichia coli O157:H7 on Field Grown
Lettuce and Treated with Spray Contaminated Irrigation Water.
Ercolani, G. L. 1979. Differential Survival of Salmonella typhi, Escherichia coli, and
Enterobacter aerogenes on Lettuce in the Field. Zentbl. Bakteriol.Naturwissensch. 134:402–411.
Evans M. R., C. D. Ribeiro, and R. L. Salmon. 2003. Hazards of Healthy Living: Bottled Water
and Salad Vegetables as Risk Factors for Campylobacter Infection. Emerg. Infect. Dis. 9:12191225.
FDA [Food and Drug Adminitration]. 1999. Guidance for Industry: Reducing Microbial Food
Safety Hazards For Sprouted Seeds. Available online at: www.fda.gov/Food/GuidanceComp
lianceRegulatoryInformation/GuidanceDocuments/ProduceandPlanProducts/ucm120244.htm
FDA [Food and Drug Administration]. 2002a. Diarrheagenic Escherichia coli. In Bacterial
Analytical Manual. Available online at: http://www.fda.gov/Food/ScienceResearch/Laboratory
Methods/BacteriologicalAnalyticalManualBAM/ucm070080.htm
FDA [Food and Drug Administration]. 2002b. Food Sampling/Preparation of Sample
Homogenate. In Bacterial Analytical Manual. Available online at: www.fda.gov/Food/Science
Research/LaboratoryMethods/BacteriologicalAnalyticalManualBAM/ucm063335.htm
FDA [Food and Drug Administration]. 2008. Cut Leafy Greens in Retail and Foodservice
Establishments. Available online at:http://fycs.ifas.ufl.edu/foodsafety/inservices/2008/handouts/
Cut%20Leafy%20Greens%20-%20Tart%20-%204-17-08.pdf
FDA [Food and Drug Administration]. 2008. Guidance for Industry: Guide to Minimize
Microbial Food Safety Hazards of Fresh-cut Fruits and Vegetables. Available online at:
http://www.fda.gov/Food/GuidanceComplianceRegulatoryInformation/GuidanceDocuments/Pro
duceandPlanProducts/UCM064458
131 FDA [Food and Drug Administration]. 2009a. Guidance for Industry: Guide to Minimize
Microbial Food Safety Hazards of Tomatoes. Available online at:www.fda.gov/Food/Guidance
ComplianceRegulatoryInformation/GuidanceDocuments/ProduceandPlanProducts/ucm173902
FDA [Food and Drug Administration]. 2009b. Guidance for Industry: Guide to Minimize
Microbial Food Safety Hazards of Leafy Greens. Available online at:
http://www.fda.gov/Food/GuidanceComplianceRegulatoryInformation/GuidanceDocuments/Pro
duceandPlanProducts/ucm174200.htm
FDA [Food and Drug Administration}. 2009c. Guidance for Industry: Guide to Minimize
Microbial Food Safety Hazards of Melons. Available online at: www.fda.gov/Food/Guidance
ComplianceRegulatoryInformation/GuidanceDocuments/ProduceandPlanProducts/ucm174171
FDA [Food and Drug Administration]. 2011. Yersinia enterocolitica. In Bad Bug Book Foodborne Pathogenic Microorganisms and Natural Toxins (2nd Ed.).Available online at:
http://www.fda.gov/food/foodsafety/foodborneillness/foodborneillnessfoodbornepathogensnatura
ltoxins/badbugbook/ucm070040.htm
FDA [Food and Drug Administration]. 2012. Salmonella Species. In Bad Bug Book - Foodborne
Pathogenic Microorganisms and Natural Toxins (2nd Ed.). pp 12-16. Available online at:
http://www.fda.gov/food/foodsafety/foodborneillness/foodborneillnessfoodbornepathogensnatura
ltoxins/badbugbook/default.htm
FDA and CFERT [Food and Drug Administration and California Food Emergency Response
Team]. 2008. Investigation of the Taco John’s Escherichia coli O157:H7 Outbreak Associated
with Iceberg Lettuce. California Department of Health, Sacramento, CA. 41p
Ferguson C. M., B. G. Coote, N. J. Ashbolt, and I. M. Stevenson. 1996. Relationships Between
Indicators, Pathogens and Water Quality in an Estuarine System. Wat. Res. 30(9):2045-2054.
Fleury M., D. F. Charron, J. D. Holt, O.B. Allen, and A. R. Maarouf. 2006. A Time Series
Analysis of the Relationship of Ambient Temperature and Common Bacterial Enteric Infections
in Two Canadian Provinces. Int. J. Biometeorol. 60:385–391.
Ferguson C., A. M. Husman, N. Altavilla, D. Deere, and N. Ashbolt. 2003. Fate and Transport of
Surface Water Pathogens in Watersheds. Crit. Rev. Environ. Sci. Technol. 33(3):299-361.
Fernandez-Alvarez R. M., S. Carballo-Cuervo, M. C. de la Rosa-Jorge, and J. Rodiguez-de
Lecea. The Influence of Agricultural Run-off on Bacterial Populations in a River. 1991. J. Appl.
Bacteriol.70: 437-442.
132 Fonseca J., S. Fallon, C. Sanchez, and K. Nolte. 2011. Escherichia coli Survival in Lettuce
Fields Following its Introduction Through Different Irrigation Systems. J. Appl. Microbiol.
110: 893–902.
Franz E. and A. H. van Bruggen. 2008. Ecology of E. coli O157:H7 and Salmonella enterica in
the Primary Vegetable Production Chain. Crit. Rev. Microbiol. 34:143-161.
Fujioka R., C. Dian-Denton, J. Castro, and K. Morphew.1999. Soil: the Environmental Source of
Escherichia coli and Enterococci in Guam’s Streams. J. Appl. Microbiol. 85:83S-89S.
Gallegos-Robles M. A., A. Morales-Loredo, G. Alvarez-Ojeda, P. A. Vega, M. Y. Chew, S.
Velarde, and P. Fratamico. 2008. Identification of Salmonella Serotypes Isolated from
Cantaloupe and Chile Pepper Production Systems in Mexico by PCR-Restriction Fragment
Length Polymorphism. J. Food Prot. 71:2217-2222.
Gannon V. P., T. A. Graham, S. Read, K. Ziebell, A. Muckie, J. Mori, J. Thomas, B. Selinger, I.
Townshed, and J. Bryne. 2004. Bacterial Pathogens in Rural Water Supplies in Southern Alberta,
Canada. J. Toxicol. Environ. Health 67:1643–1653.
Geldreich E. E. 1975. Evaluating Water Bacteriological Laboratories, pg 119-120.
Environmental Protection Agency, Cincinnati.
Gelting, R. 2007. Investigation of an Escherichia coli O157:H7 Outbreak Associated with Dole
Pre-Packaged Spinach. Available online at: http://www.cdc.gov/nceh/ehs/Docs/Investigation_of_
an_E_Coli_Outbreak_Associated_with_Dole_Pre-Packaged_Spinach.pdf
Global GAP. 2010. Integrated Farm Assurance: Fruits and Vegetables. Available online at:
http://www.globalgap.org/cms/front_content.php?idart=147&idcat=48&lang=1&client=1
Greene S. K., E. R. Daly, E. A. Talbot, L. J. Demma, S. Holzbauer, N. J. Patel, T. A. Hill, M. O.
Walderhaug, R. M. Hoekstra, M. F. Lynch and J. A. Painter. 2008. Recurrent Multistate
Outbreak of Salmonella Newport Associated with Tomatoes from Contaminated Fields.
Epidemiol. Infect.136:157-165.
Griffin D. W., E. K. Lipp, M. R. McLaughlin, J. B. Rose. 2001. Marine Recreation and Public
Health Microbiology: Quest for the Ideal Indicator. Bioscience 51(10):817-826.
Guo X., J. Chen, L. R. Beuchat, and R. E. Brackett. 2000. PCR Detection of Salmonella enterica
serotype Montevideo in and on Raw Tomatoes Using Primers Derived from hilA. Appl. Environ.
Microbiol. 66:5248-5252
133 Guo X.,M. W. Van Iersel, J. Chen, R. E. Brackett, and L. R. Beuchat. 2002. Evidence of
Association of Salmonellae with Tomato Plants Grown Hydroponically in Inoculated Nutrient
Solution. Appl. Environ. Microbiol. 68:3639–3643.
Haley B. J., D. J. Cole, and E. K. Lipp. 2008. Distribution, Diversity, and Seasonality of
Waterborne Salmonellae in a Rural Watershed. Appl. Environ. Microbiol. 75(5):1248-1255.
Hanning I. B., J. D. Nutt, and S. C. Ricke. 2009. Salmonellosis Outbreaks in the United States
Due to Fresh Produce: Sources and Potential Intervention Measures. Foodborne Pathog. Dis.
6(6): 635-648.
Harwood V. J., A. D. Levine, T. M. Scott, V. Chivukula, J. Lukasik, S. R. Farrah, and J. B. Rose.
2004. Validity of the Indicator Organism Paradigm for Pathogen Reduction in Reclaimed Water
and Public Health Protection. Appl. Environ. Microbiol 71(6):3163-3170.
Hilborn E. D., J. H. Mermin, P. A. Mshar, J. L. Hadler, A. Voetsch, C. Wojtkunski, M. Swartz,
R. Mshar, M. A. Lambert-Fair, J. A. Farrar, M. K. Glynn, and L. Slutsker. 1999. A Multistate
Outbreak of Escherichia coli O157:H7 Infections Associated with Consumption of Mesculin
Lettuce. Arch. Inter. Med 159:1758-1764.
Hӧrman A., R. Rimhanen-Finne, L. Maunula, C. H. von Bonsdorff, N. Torvela, A. Heikinheimo
and M. L. Hӓnninen. 2004. Campylobacter spp., Giardia spp., Cryptosporidium spp.,
Noroviruses, and Indicator Organisms in Surface Water in Southwestern Finland, 2000-2001.
Appl. Environ. Microbiol. 70(1):87-95.
Hӧrman A., and M. L. Hӓnninen. 2006. Evaluation of the Lactose Tergitol-7, m-Endo LES,
Colilert 18, Readycult Coliforms 100, Water-Check-100, 3M Petrifilm EC and DryCult Coliform
Test Methods for Detection of Total Coliforms and Escherichia coli in Water Samples. Water
Research 40:3249-3256.
Hsu B. M., K. H. Huang, S. W. Huang, K. C. Tseng, M. J. Lin, D. D. Ji, F. C. Shih, J. L. Chen,
and P. M. Kao. 2011. Evaluation of Different Analysis and Identification Methods for
Salmonella Detection in Surface Drinking Water Sources. Sci Total Environ. 409(20):4435-41.
Ibenyassine K., R. Ait-Mhand, Y. Karamoko, N. Cohen, and M. M. Ennaji. 2005. Use of
Repetitive DNA Sequences to Determine the Persistence of Enteropathogenic Escherichia coli in
Vegetables and in Soil Grown in Fields Treated with Contaminated Irrigation Water. Lett Appl.
Microbiol. 43(5):528-533.
134 IDEXX Laboratories. 2012. A Comparison of IDEXX Coliform and E. coli Tests. Available
online at: http://www.idexx.com/view/xhtml/en_us/water/colisure.jsf?SSOTOKEN=0
Ijabadeniyi O. A., L. K. Debusho, M. Vanderlinde, and E. M. Buys. 2011. Irrigation Water as a
Potential Preharvest Source of Bacterial Contamination of Vegetables. J. Food Safety 31(4):452461.
Islam M., J. Morgan, M. P. Doyle, S. C. Phatak, P. Millner, and X. Jiang. 2004a. Persistence of
Salmonella enterica Serovar Typhimurium on Lettuce and Parsley and in Soils on Which They
Were Grown in Fields Treated with Contaminated Manure Composts or Irrigation Water.
Foodborne Pathog. Dis. 1(1):27-35.
Islam M., M. P. Doyle, S. C. Phatak, P. Millner, and X. Jiang. 2004b. Persistence of
Enterohemorrhagic Escherichia coli O157:H7 in Soil and on Leaf Lettuce and Parsley Grown in
Fields Treated with Contaminated Manure Composts or Irrigation Water. J. Food Protect.
67(7):1365-1370.
Islam M., M. P. Doyle, S. C. Phatak, P. Millner, X. Jiang. 2005. Survival of Escherichia coli
O157:H7 in Soil and on Carrots and Onions Grown in Fields Treated with Contaminated Manure
Composts or Irrigation Water. Food Microbiol. 22:63-70.
Itoh Y., Y. Sugita–Konishi, F. Kasuga, M. Iwaki, Y. Hara-Kudo, N. Saito, Y Noguchi, H.
Konuma, and S. Kumagai. 1998. Enterohemorrhagic Escherichia coli O157:H7 present in radish
sprouts. Appl. Environ. Microbiol.64:1532–1535.
Jablasone J., K. Warriner, M. Griffiths. 2005. Interactions of Escherichia coli O157:H7,
Salmonella typhimurium and Listeria monocytogenes plants cultivated in a gnotobiotic system.
International Journal of Food Microbiology 99(1):7–18.
Jiang S., R. Noble, and W. Chu. 2001. Humanadenoviruses and coliphages in urban runoffimpacted coastal waters of Southern California. Appl. Environ. Microbiol. 67:179-184.
Johannessen G. S., G. B. Bengtsson, B. T. Heier, S. Bredholt, Y. Wasteson, and L. M. Rorvik.
2005. Potential Uptake of Escherichia coli O157:H7 from Organic Manure into Crisphead
Lettuce. Appl. Environ. Microbiol. 71(5): 2221-2225.
Johnson J. Y. M., J. E. Thomas, T. A. Graham, I. Townshend, J. Byrne, L. B. Selinger, and V. P.
J. Gannon. 2003. Prevalence of Escherichia coli O157:H7 and Salmonella spp. in Surface
Waters of Southern Alberta and its Relation to Manure Sources. Can. J. Microbiol. 49: 326-335.
135 Jones G. E., P. M. Franklin, and S. B. Thomas. 1950. The Effect of Overnight Refrigeration on
the Results of the Bacteriological Examination of Farm Water Supplies. Nature, Lond. 166:896.
Kindhauser, M. K. 2003. Communicable diseases 2002: Global defence against the infectious
disease threat. IWA Publishing:WHO. Available online at: http://whqlibdoc.who.int/
publications/2003/9241590297.pdf
Klerks, M. M., E. Franz, M. van Gent-Pelzer, C. Zijlstra, and A. H. van Bruggen. 2007.
Differential Interaction of Salmonella enterica Serovars with Lettuce Cultivars and Plant–
Microbe Factors Influencing the Colonization Efficiency. The ISME Journal 1:620–631.
Kovats R. S., S. J. Edwards, D. Charron, J. Cowden, R. M. D’Souza, K. L. Ebi, C. Gauci, P. G.
Smidt, S. Hajat, S. Hales, G. H. Pezzi, B. Kriz, K. Kutsar, P. McKeown, K. Mellou, B. Menne, S.
O’Brien, W. van Pelt, and H. Schmidt. 2005. Climate Variability and Campylobacter Infection:
an International Study. Int. J. Biometeorol. 49:207–214.
Kramer M., B. Herwaldt, G. Craun, R. Calderon, and D. Juranek. 1996. Surveillance for
Waterborne-Disease Outbreaks—United States, 1993–1994. MMWR Surveill. Summ. 12:1–33.
Kudaka J., T. Horii, K. Tamanaha, K, Itokazu, M. Nakamura, K. Taira, M. Nidaira, S. Okano,
and A. Kitahara. 2010. Evaluation of the Petrifilm Aerobic Count Plate for Enumeration of
Aerobic Marine Bacteria from Seawater and Caulerpa lentillifera. J. Food Protect. 73(8):15291532.
Kutter S., A. Hartmann, and M. Schmid. 2006. Colonization of barley (Hordeum vulgare) with
Salmonella enterica and Listeria spp. FEMS Microbiol. Ecol. 56:262–271.
Lapidot A., and S. Yaron. 2009. Transfer of Salmonella enterica serovar Typhimurium from
contaminated irrigation water to parsley is dependent on curli and cellulose, the biofilm matrix
components. J. Food Protect. 72(3):618–623.
Leclerc H., L. Schwartzbrod, and E. Dei-Cas. 2002. Microbial agents associated with waterborne diseases. Crit. Rev, Microbiol. 28(4):371–409.
Lemarchand K., and P. Lebaron. 2003. Occurrence of Salmonella spp. and Cryptosporidium spp.
in a French Coastal Watershed: Relationship with Fecal Indicators. FEMS Microbiol. Lett.
218:203-209.
136 Levantesi C., L. Bonadonna, R. Briancesco, E. Grohmann, S. Toze, and V. Tandoi. 2011.
Salmonella in Surface and Drinking Water: Occurrence and Water-Mediated Transmission. Food
Res. Int. 45(2): 587-602.
LGMA [Leafy Greens Marketing Agreement]. 2012. Commodity Specific Food Safety
Guidelines for the Production and Harvest of Lettuce and Leafy Greens. January 20, 2012.
Sacramento, CA. Available online at: www.caleafygreens.ca.gov/
Mahon B. E., A. Ponka, W. N. Hall, K. Komatsu, S. E. Dietrich, A. Siitonen, G. Cage, P. Hayes,
M. A. Lambert-Fair, N. H. Bean, P. M. Griffen, and L. Slutsker.1997. An International Outbreak
of Salmonella Infections Caused by Alfalfa Sprouts Grown from Contaminated Seeds. J. Infect
Dis. 175:876-882.
Materos L. A., M. M. Garcia, and V. McDonald. 2007. Identification of Sources of Microbial
Pathogens on Cantaloupe Rinds from Pre-Harvest Operations. World J. Microbiol. Biotechnol.
23:1281-1287.
Mattelet C. 2005. Household Ceramic Water Filter Evaluation Using Three Simple Low-Cost
Methods: Membrane Filtration, 3M Petrifilm and Hydrogen Sulfide Bacteria in Northern Region,
Ghana. DSpace@MIT: Massachusetts Institute of Technology. Available online at:
http://hdl.handle.net/1721.1/34669
McCarthy J. A. 1957. Storage of Water Samples for Bacteriologic Examinations. Am J Public
Health Nations Health 47(8): 971–974.
McDaniels A. E., R. H. Bordner, P. S. Gartside, J. R. Haines, K. P. Brenner, and C. C. Rankin.
1985. Holding Effects on Coliform Enumeration in Drinking Water Samples. Appl. Envrin.
Microbiol. 50:755-762.
McEwen A., C. Dawson, and L. Gerstenberger. 2011. Adirondack Park Forest Preserve Carrying
Capacity of Water Bodies Study: Phase 1 – Selecting Indicators for Monitoring Recreational
Impacts. SUNY College of Environmental Science and Forestry. Available online at:
http://www.esf.edu/nywild/publications/docs/carrying-capacity.pdf
McGee P., D. J. Bolton, J. J. Sheridan, B. Earley, G. Kelly, and N. Leonard. 2002. Survival
of Escherichia coli O157:H7 in Farm Water: its Role as a Vector in the Transmission of the
Organism within Herds. J. Appl. Microbiol. 93:706–713.
137 Michino H., K. Araki, S. Minami, S. Takaya, N. Sakai, M. Miyazaki. A. Ono, and H. Yanagawa.
1999. Massive Outbreak of Escherichia coli O157:H7 Infection in Schoolchildren in Sakai City,
Japan, Associated with Consumption of White Radish Sprouts. Am. J. Epidemiol 150:787-796.
Mohle-Boetani J., B. Werner, and M. Polumbo. 2002. Outbreak of Salmonella seotype Kottbus
Infections Associated with Eating Alfalfa Sprouts—Arizona, California, Colorado, and New
Mexico, February–April 2001. Morb. Mortal. Wkly. Rep. 51:7–9.
Mouslim C., F. Hibert, H. Huang, and E. A. Groisman. 2002. Conflicting Needs for a Salmonella
Hypervirulence Gene in Host and Non-Host Environments. Mol. Microbiol. 45:1019–1027.
Mull B. and V. R. Hill. 2009. Recovery and Detection of Escherichia coli O157:H7 in Surface
Water, Using Ultrafiltration and Real-Time PCR. Appl. Environ. Microbiol. 75(11): 3593-3597.
Naumova E. N., J. S. Jjagai, B. Matyas, A. DeMaria, I. B. MacNeill, and J. K. Griffiths. 2006.
Seasonality in Six Enterically Transmitted Diseases and Ambient Temperature. Epidemiol.
Infect. 135:1–12.
Neill M. A., P. I. Tarr, C. R. Clausen, D. L. Christie, and R. O. Hickman. 1987. Escherichia
coli O157:H7 as the Predominant Pathogen Associated with the Hemolytic Uremic Syndrome: a
Prospective Study in the Pacific Northwest. Pediatrics 80:37–40.
Noble R. T., and J. A. Fuhrman. 2001. Enteroviruses Detected by Reverse Transcriptase
Polymerase Chain Reaction from the Coastal Waters of Santa Monica Bay, California: Low
Correlation to Bacterial Indicator Levels. Hydrobiologia 460:175-184.
North American Tomato Trade Working Group. 2008. Available online at: http://www.wga.com/
DocumentLibrary/Tomato%20Guidelines%20July08%20FINAL.pdf
Nwachuku N., and C. P. Gerba. 2008. Occurrence and Persistence of Escherichia coli O157:H7
in Water. Rev. Environ. Sci. Biotechnol. 7:267-273.
O’Brien E. 2006. Volunteers Conduct Bacteria Methods Comparison Study. The Volunteer
Monitor: The National Newsletter of Volunteer Watershed Monitoring 18(1):1-6.
Okhuysen P., C. L. Chappell, J. H. Crabb, C. R. Sterling, and H. L. DuPont. 1999. Virulence of
three dinstinct Cryptosporidium parvum isolates for healthy adults. J. Infect. Dis. 180:1275–
1281.
138 Olsen, S., R. Bishop, F. Brenner, T. Roels, N. Bean, R. Tauxe, and L. Slutsker. 2001. The
Changing Epidemiology of Salmonella: Trends in Serotypes Isolated from Humans in the United
States, 1987–1997. J. Infect. Dis. 183:753–761.
Olsen S. J., G. Miller, T. Breuer, M. Kennedy, C. Higgins, J. Walford, G. McKee, K. Fox, W.
Bibb, and P. Mead. 2002. A Waterborne Outbreak of Escherichia coli O157:H7 Infections and
Hemolytic Uremic Syndrome: Implications for Rural Water Systems. Emerg Infect Dis 8:370–
375.
OMAFRA [Ontario Ministry of Agriculture, Food, and Rural Affairs]. 2010. Improving OnFarm Food Safety Though Good Irrigation Practices. Available online at:
http://www.omafra.gov.on.ca/english/crops/facts/10-037.htm
Pezzoli L., R. Elson, C. L. Littl, H. Yip, I. Fisher, R. Yishai, E. Anis, L. Valinsky, M.
Biggerstaff, N. Patel, H. Mather, D. J. Brown, J. E. Coia, W. van Pelt, E. M. Nielsen, S.
Ethelberg, E. de Pinna, M. D. Hampton, T. Peters, J. Threlfall. 2008. Packed with Salmonella —
Investigation of an international outbreak of Salmonella Senftenberg infection linked to
contamination of prepacked basil in 2007. Foodborne Pathog. Dis. 5:661–668.
Polo F., M. J. Figueras, I. Inza, J. Sala, J. M. Fleisher, and J. Guarro.1998. Relationship Between
Presence of Salmonella and Indicators of Faecal Pollution in Aquatic Habitats. FEMS Microbiol.
Lett. 160:253-256.
Pond, K. 2005. Water recreation and disease infections: Plausibility of Associated Acute Effects,
Sequelae and Mortality. IWA Publishing: WHO. Available online at: http://www.who.int/water_
sanitation_health/bathing/recreadis.pdf
Pope M. L., M. Bussen, M. A. Feige, L. Shadix, S. Gonder, C. Rodgers, Y. Chambers, J. Pulz, K.
Miller, K. Connell, and J. Standridge. 2003. Assessment of the Effects of Holding Time and
Temperature on Escherichia coli Densities in Surface Water Samples. Appl. Environ. Microbiol.
69(10):6201-6207.
Power, M. L., J. Littlefield-Wyer, D. M. Gordon, D. A. Veal, and M. B. Slade. 2005. Phenotypic
and Genotypic Characterization of Encapsulated Escherichia coli Isolated from Blooms in Two
Australian Lakes. Environ. Microbiol. 7:631-640.
Priego R., L. M. Medina, and R. Jordano. 2000. Evaluation of Petrifilm Series 2000 as a Possible
Rapid Method to Count Coliforms in Foods. J. Food Protect. 63:1137-1140.
139 Primus 2007. PrimusLabs.com Version 07.04 Ranch Audit Guidelines. Available online at:
http://www.primuslabs.com/docs/guidelines/v0704ranchauditguidelines050107.pdf
Primus 2007. PrimusLabs.com Version 07.08 Greenhouse Audit Guidelines. Available online at: http://www.primuslabs.com/docs/guidelines/v07.08GHAuditornotesrev102307.pdf
PMA and UFVA [Produce Marketing Association and United Fresh Fruit and Vegetable
Association]. 2005. Commodity Specific Food Safety Guidelines for the Melon Supply Chain.
Available online at: www.wga.com/DocumentLibrary/MelonGuidanceDocument[2005].pdf
Produce Safety Project. 2010. Breakdown: Lessons to Be Learned from the 2008 Salmonella
Saintpaul Outbreak. Available online at: http://www.producesafetyproject.org/reports?id=0001
Rai P. K., and B. D. Tripathi. 2007. Microbial Contamination in Vegetables Due to Irrigation
with Partially Treated Municipal Wastewater in a Tropical City. Int. J. Environ. Health Res.
17:389-395.
Rangel J. M., P. H. Sparling, C. Crowe, P. M. Griffen, and D. L. Swerdlow. 2005. Epidemiology
of Escherichia coli O157:H7 Outbreaks, United States, 1982-2002. Emerg. Infect. Dis.
11(4):603-609.
Reddy C. A., T.J. Beveridge, J.A. Breznak, G.A. Marzluf, T.M. Schmidt, and L.R. Snyder. 2007.
Nutrition and Media. In Methods and General and Molecular Microbiology pp 200-214,
American Society for Microbiology Press, Washington, DC.
Reddy K. R., R. Khaleel, and M. R. Overcash. 1981. Behavior and Transport of Microbial
Pathogens and Indicator Organisms in Soils Treated with Organic Waste. J. Environ Qual.
10:255-265.
Rhodes M. W., and H. Kator. 1988. Survival of Escherichia coli and Salmonella spp. in
Estuarine Environments. Appl. Environ. Microbiol. 54:2902–2907.
Riley L. W., R. S. Remis, S. D. Helgerson, H. B. McGee, J. G. Wells, B. R. Davis, R. J. Hebert,
E. S. Olcott, L. M. Johnson, N. T. Hargrett, P. A. Blake, and M. L. Cohen. 1983. Hemorrhagic
Colitis Associated with a Rare Escherichia coli Serotype. N. Engl. J. Med. 308:681–685.
Russel S. M. 2000. Comparison of the Traditional Three-Tube Most Probable Number Method
with the Petrifilm, Simplate, BioSys Optical, and Bactometer Conductance Methods for
Enumerating Esherichia coli from Chicken Carcasses and Ground Beef. J. Food Protect.
63:1179-1183.
140 Santo Domingo J. W., S. Harmon, and J. Bennett. 2000. Survival of Salmonella Species in
Water. Curr. Microbiol. 40:409-417.
Scallan E., R. M. Hoekstra, F. J. Angulo, R. V. Tauxe, M. A. Widdowson, S. L. Roy, J. L. Jones,
and P. M. Griffin. 2011. Foodborne Illness Acquired in the United States—Major Pathogens.
Emerg. Infect. Dis. 17:126–128
Schikora A., A. Carreri, E. Charpentier, and H. Hirt. 2008. The Dark Side of the Salad:
Salmonella typhimurium Overcomes the Innate Immune Response of Arabidopsis Thaliana and
Shows an Endopathogenic Lifestyle. PloS One 3(5):e2279.
Schraft H., and L. A. Watterworth. 2005. Enumeration of Heterotrophs, Fecal Coliforms and
Escherichia coli in Water: Comparison of 3M Petrifilm Plates with Standard Plating Procedures.
J. Microbiol. Methods 60(3):35-42.
Selvakumar A., M. Borst, M. Boner, and P. Mallon. 2004. Effects of Sample Holding Time on
Concentrations of Microorganisms in Water Samples. Water Environ. Res. 76(1):67-72.
Shakalisava Y., C. Doherty, W. Hahnel, D. Diamond. 2010. A Survey of the Microbiological
Water Quality of Coastal and Fresh Waters in the Dublin Area. Marine Institute Beaufort
Sensors Initiative. Available online at: http://www.computing.dcu.ie/~adoherty/environment/
water_quality_Survey-Final_version_May_2010.pdf
Shelton D. R., J. S. Karns, C. Coppock, J. Patel, M. Sharma, and Y. A. Pachepsky. 2011.
Relationship Between eae and stx Virulence Genes and Escherichia coli in an Agricultural
Watershed: Implications for Irrigation Water Standards and Leafy Green Commodities. J. Food
Protect. 74(1):18-23.
Siegler R. L., A. T. Pavia, R. D. Christofferson, and M. K. Milligan. 1994. A 20-year
Population-Based Study of Postdiarrheal Hemolytic Uremic Syndrome in Utah. Pediatrics
94:35–40.
Sinclair R. G., J. B. Rose, S. A. Hashsham, C. P. Gerba, and C. N. Haas. 2012. Criteria for
Selection of Surrogates Used to Study the Fate and Control of Pathogens in the Environment.
Appl. Environ. Microbiol. 78(6):1969-1977.
Sivapalasingam S., C. R. Friedman, L. Cohen, and R. V. Tauxe. 2004. Fresh Produce: A
Growing Cause of Outbreaks of Foodborne Illness in the United States, 1973 Through 1997. J.
Food Protect. 67(10):2342-2353.
141 Smith W. A., J. A. Mazet, and D. C. Hirsh. 2002. Salmonella in California Wildlife Species:
Prevalence in Rehabilitation Centers and Characterization of Isolates. J. Zoo Wildl. Med.
33:228–235.
Sӧderstrӧm A., P. Ӧsterberg, A. Lindqvist, B. Jӧnsson, A. Lindberg, S. B. Ulander, C. WelinderOlsson, S. Lӧfdahl, B. Kaijser, B. de Jong, S. Kühlmann-Berenzon, S. Boqvist, E. Eriksson, E.
Szanto, and S. Andersson. 2008. A Large Escherichia coli O157 Outbreak in Sweden Associated
with Locally Produced Lettuce. Foodborne Pathog. Dis. 5:339-349.
Solomon E. B., C. J. Potenski, and K. R. Matthews. 2002a. Effect of Irrigation Method on
Transmission to and Persistence of Escherichia coli O157:H7 on Lettuce. J. Food Protect.
65(4):673-676.
Solomon, E.B., S. Yaron, and K. R. Matthews. 2002b. Transmission of Escherichia coli
O157:H7 from contaminated manure and irrigation water to lettuce plant tissue and its
subsequent internalization. Appl. Environ. Microbiol.68:397–400.
Srikantiah P., J. C. Lay, S. Hand, J. A. Crump, J. Campbell, M. S. Van Duyne, R. Bishop, R.
Middendor, M. Currier, P. S. Mead, and K. Mølbak. 2004. Salmonella enterica Serotype Javiana
Infections Associated with Amphibian Contact, Mississippi, 2001. Epidemiol. Infect. 132:273–
281.
Standridge J. H., and D. J. Lesar. 1977. Comparison of Four-Hour and Twenty-Four-Hour
Refrigerated Storage of Nonpotable Water for Fecal Coliform Analysis. Appl. Environ.
Microbiol. 34(4): 398-402.
Steele M., A. Mahdi, and J. Odumeru. 2005. Microbial Assessment of Irrigation Water Used for
Production of Fruit and Vegetables in Ontario, Canada. J. Food Protect. 68(7):1388-1392.
Steele M. and J. Odumeru. 2004. Irrigation Water as Source of Foodborne Pathogens on Fruit
and Vegetables. J. Food Protect. 67(12): 2839-2849.
Stepenuck K. F., L. G. Wolfson, B. W. Liukkonen, J. M. Iles, and T. S. Grant. 2011. Volunteer
Monitoring E. coli in Streams of the Upper Midwestern United States: A Comparison of
Methods. Environ. Monit. Assess. 174:625-633.
Suslow T. 2010. Standards For Irrigation and Foliar Contact Water. Peer reviewed issue brief
available online at: http://www.producesafetyproject.org/admin/assets/files/Water-Suslow-1.pdf
142 Takkinen J., U. M. Nakari, T. Johansson, T. Niskanen, A. Siitonen, and M. Kuusi. 2005. A
Nationwide Outbreak of Multiresistant Salmonella Typhimurium var. Copenhagen DT104B
Infection in Finland Due to Contaminated Lettuce from Spain, May 2005. Eurosur.
10(26):pii=2734.
Tauxe R., H. Kruse, C. Hedberg, M. Potter, J. Madden, and K. Wachsmuth. 1997. Microbial
Hazards and Emerging Issues Associated with Produce: A Preliminary Report to the National
Advisory Committee on Microbiologic Criteria for Foods. J. Food Protect. 60:1400–1408.
Teplitski M., J. D. Barak, and K. R. Schneider. 2009. Human Enteric Pathogens in Produce: UnAnswered Ecological Questions with Direct Implications for Food Safety. Curr. Opin.
Biotechnol. 20:166-171.
Thunberg R. L., T. T. Tran, R. W. Bennett, and R. N. Matthews. 2002. Microbial Evaluation of
Selected Fresh Produce Obtained at Retail Markets. J. Food Protect. 65:677–682.
Tournas V. H. 2005. Spoilage of Vegetable Crops by Bacteria and Fungi and Related Health
Hazards. Crit. Rev. Microbiol. 31:33–44.
Uesugi A. R., M. D. Danyluk, R. E. Mandrell, and L. J. Harris. 2007. Isolation of Salmonella
Enteritidis Phage Type 30 from a Single Almond Orchard Over a 5-Year Period. J. Food Protect.
70:1784-1789.
United Produce Association. 2009. Food Safety Programs and Auditing Protocol for the Fresh
Tomato Supply Chain. Available online at: http://www.unitedfresh.org/assets/tomato_metrics/
Open_Field_Checklist.pdf
USDA [United State Department of Agriculture]. 1998. USDA Fresh Fruit and Vegetable Audit
Programs. Available online at: http://www.ams.usda.gov/AMSv1.0/ams.fetchTemplateData.do?
template=TemplateN&page=GAPGHPAuditVerificationProgra
USDA [United States Department of Agriculture]. 2007. Good Agricultural Practices and Good
Handling Practices Audit Verification Program. USDA, Washington, DC.
USDA NASS [United States Department of Agriculture National Agricultural Statistics Service].
2009. 2007 Census of Agriculture, Specialty Crops Volume 2. Available online at:
www.agcensus.usda.gov/Publications/2007/Online_Highlights/Specialty_Crops/speccrop.pdf
143 USDA NOP [United States Department of Agriculture National Organic Program]. 2010.
Guidance and Instructions for Accredited Certifying Agents and Certified Operations. Available
online at: http://www.ams.usda.gov/AMSv1.0/getfile?dDocName=STELPRDC5086323
USDA and FDA/CFSAN [United States Department of Agriculture and Food and Drug
Administration/Center for Food Safety and Applied Nutrition]. 1998. Guidance for Industry
Guide to Minimize Microbial Food Safety Hazards for Fresh Fruits and Vegetables. Available
online at: http://www.fda.gov/food/guidancecomplianceregulatoryinformation/guidance
documents/produceandplanproducts/ucm064458.htm
Vail J. H., R. Morgan, C. R. Merino, F. Gonzales, R. Miller, and J. L. Ram. 2003. Enumeration
of Waterborne Escherichia coli with Petrifilm Plates: Comparison to Standard Methods. J.
Environ. Qual. 32:368-373.
Van Houten R., D. Farberman, J. Norton, J. Ellison, J. Kiehlbach, T. Morris, and P. Smith. 1998.
Plesiomonas shigelloides and Salmonella serotype Hartford Infections Associated with a
Contaminated Water Supply—Livingston County, New York. Morb. Mortal. Wkly. Rep.
47:394–396.
Wahlström H., E. Tysen, E. Olsson-Engvall, B. Brandström, E. Eriksson, T. Mörner, and I.
Vågsholm. 2003. Survey of Campylobacter species, VTEC O157 and Salmonella species in
Swedish wildlife. Vet. Rec. 153:74-80.
Walters S. P., V. P. Gannon, and K. G. Field. 2007. Detection of Bacteroidales Fecal Indicators
and the Zoonotic Pathogens E. coli O157:H7, Salmonella, and Campylobacter in River Water.
Environ. Sci. Technol. 41:1856-1862.
Warriner K., S. Spaniolas, M. Dickinson, C. Wright, and W. M. Waites. 2003. Internalization of
bioluminescent Escherichia coli and Salmonella Montevideo in growing bean sprouts. J. Appl.
Microbiol. 95:719–727.
Weagant S. D. and A. J. Bound. 2001. Evaluation of Techniques for Enrichment and Isolation of
Escherichia coli O157:H7 from Artificially Contaminated Sprouts. Intern J. of Food Microbiol.
71:87-92.
Weagant S. D., J. L. Bryant, and K. G. Jinneman. 1994. An Improved Rapid Technique for
Isolation of Escherichia coli O157:H7 from Foods. J. Food Protection 58:7-12.
144 Wen J., X. Deng, Z. Li, E.G. Dudley, R.C. Anantheswaran, S.J. Knabel, and W. Zhang. 2011.
transcriptomic Response of Listeria monocytogenes During the Transition to the Long-TermSurvival Phase. Appl. Environ. Microbiol. 77(17): 5966-5972.
Westrell T., N. Ciampa, F. Boelaert, B. Helwigh, H. Korsgaard, M. Chriel, A. Ammon, and P.
Makela. 2009. Zoonotic Infections in Europe in 2007: A Summary of the EFSA-ECDC Annual
Report. Euro Surveill 14(3):pii19100
Wetzler T. F., and J. Allard. 1977. Yersinia enterocolitica from Trapped Animals in Washington
State. Paper presented at International Conference on Disease in Nature Communicable to Man.
Yellow Bay, Montana.
WGA [Western Growers Association]. 2010. Commodity Specific Food Safety Guidelines for
the Production, Harvest, Post-Harvest, and Value Added Unit Operations of Green Onions.
Available online at:
http://www.wga.com/DocumentLibrary/scienceandtech/100226%20Final%20versionProductionHarvest,%20Postharvest,%20Value-Added-Accessible.pdf
Wheeler Alm E., J. Burke and A. Spain. 2003. Fecal Indicator Bacteria are Abundant in Wet
Sand at Freshwater Beaches. Water Res. 37:3978-3982.
Whitman, R. L., D. A. Shively, H. Pawlick, M. B. Nevers, and M. N. Byappanahalli. 2003.
Occurrence of Escherichia coli and Enterococci in Cladophora (Chlorophyta) in Nearshore
Water and Beach Sand of Lake Michigan. Appl. Environ. Microbiol.69:4714-4719.
WHO [World Health Organization]. 1989. Health Guidelines for the Use of Wastewater in
Agriculture and Aquaculture. Available online at:
http://www.who.int/water_sanitation_health/dwq/iwachap2.pdf
WHO [World Health Organization]. 1993. WHO Guidelines for Drinking Water Quality, 2nd ed.
Recommendations, vol. 1. IWA Publishing:WHO. Available online at:
http://www.who.int/water_sanitation_health/dwq/gdwq2v1/en/
Wohlsen T., J. Bates, G. Vesey, W. A.Robinson, and M. Katouli. 2005. Evaluation of the
Methods for Enumerating Coliform Bacteria from Water Samples Using Precise Reference
Standards. Lett. Appl. Microbiol. 42:350-356.
Wright, R. 1989. The Survival Patterns of Selected Faecal Bacteria in Tropical Fresh Waters.
Epidemiol. Infect. 103:603–611.
145 Zhuang R.-Y., L. R. Beuchat, and F. J. Angulo. 1995. Fate of Salmonella montevideo on and in
Raw Tomatoes as Affected by Temperature and Treatment with Chlorine. Appl. Environ.
Microbiol. 61:2127–2131.