EVALUATION OF TOTAL COLIFORM, FECAL COLIFORM, AND
FECAL STREPTOCOCCI AS ADEQUATE INDICATORS IN
MONITORING PUBLIC WATER SUPPLY QUALITY IN THE
TROPICS
by
Jose Marcos Soto Munoz
A Thesis submitted to the Department of BiologN
FACULTY OF NATURAL SCIENCES
UNIVERSITY OF PUERTO RJCO
RIO PDEDRAS CAMPUS
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE IN BIOLOGY
February, 1989
Ri'o Piedras, Puerto Rico
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This thesis has been accepted by the faculty of the;
DEPARTMENT OF BIOLOGY
FACULTY OF NATURAL SCIENCES
UNIVERSITY OF PUERTO RICO
RIO PIEDRAS CAMPUS
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE IN BIOLOGY
Thesis Committee:
Advisor
Terry C. Hazen, Ph. D.
\ Gary A. Toranzos^
Ivette Garcfa Castro, Ph. D.
__
__,
Sharon K. File, Ph. D.
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DEDICATION
When I want to discover something, I begin by reading up everything
that has been done along that line in the past - that's what all the books
in the library arc for. I see what has been accomplished at great labor
and expense in the past. I gather the data of many experiments as a
starting point and then I make many more. The three essentials to
achieve anything worthwhile are, first, hard work; second, stick-to-it-
iveness; third, common sense.
-Thomas A. Edison
To those who have thought they can't.
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ACKNOWDLEGMENTS
It is impossible to properly thank all the people that helped and
supported me during the course of this project in so little space. To all
of you, thanks. You know who you are.
My parents, Jose Luis and Carmen Ana, were a constant source of
support. So were the rest of my family: my grandmother, Marcolina;
my brothers, Jor;i Luis and Juan Sandalio; my sister-in-law, Doris; and
my niece, Carla.
I thank Dr. Ivette Garcia-Castro, Dr. Sharon K. File, and Dr. Gary
Toranzos for their contributions as part of my thesis committee.
My good friends from the graduate program, especially Maria
Socorro Flores, Patricia Marcos (and John Calderon), and Maggie Rivera
provided much needed encouragement and good laughs. Although they
were not physically close, Ivette Lopez and Tom Abbruscato gave me
not only their friendship, but also their support through letters, phone
calls, and occasional visits. The staff at the Microbial Ecology
Laboratory, especially Luz E. Rodriguez, Madeline Bermudez, Ismael
Perez, and Yazmfn Rojas, always provided useful suggestions, technical
advice, cheerfulness, and friendly help.
I thank all at EPA's Caribbean Field Office, especially Racqueline
Shelton, Jorge Martinez and the rest of the Water Management Staff, for
their unconditional support and understanding.
My deepest gratitude goes to my advisor, Dr. Terry C. Hazen. for
giving me this opportunity and then guiding me across great distance.
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though we were far apart, he was always available for questioning
and made many useful comments and suggestions. Thanks a lot!
This research was supported in part by the U. S. Environmental
Protection Agency, Water Management Division; Region II, Caribbean
Fkid Office.
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TABLE OF CONTENTS
Page No.
Dedication „..,... „ .. ii
Acknowdlegments .iii
Table of Contents _ „ ~- „ v
List of Tables..., vi
List of Figures viii
Abstract ix
Introduction 1
Materials and Methods 1 7
Results 27
Discussion _ „ 3 6
Conclusions „ 5 7
Literature Cited „ 5 9
Tables 72
Figures 9 2
Appendix I ! 0 2
Appendix II 1 0 3
Appendix III 1 0 4
Appendix IV .„ - - , 1 0 5
Appendix V _ _ ~ 1 0 6
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LIST OF TABLES
Page No.
f$ble 1. Water Quality Parameter Averages 7 2
Table 2. Bacterial Density Averages .-* 7 3
Table 3. Direct Count and Percent Activity Averages 74
Table 4. Correlation Matrix: Combined Influent Bacteriological
Methods and Water Quality 7 5
Table 5. Correlation Matrix: Bayamon Influent Bacteriological
Methods and Water Quality 7 6
Table 6. Correlation Matrix: Bayamon Effluent Bacteriological
Methods and Water Quality .........7 7
Table 7. Correlation Matrix: Guaynabo Influent Bacteriological
Methods and Water Quality 7 8
Table 8. Correlation Matrix: Guaynabo Effluent Bacteriological
Methods and Water Quality 7 9
Table 9. Correlation Matrix: Villalba Influent Bacteriological
Methods and Water Quality 8 0
Table 10. Correlation Matrix: Villalba Effluent Bacteriological
Methods and Water Quality 8 1
Table 11. Correlation Matrix: Yauco Influent Bacteriological
Methods and Water Quality 8 2
Table 12. Correlation Matrix: Yauco Effluent Bacteriological
Methods and Water Quality 8 3
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Table 13. Media Performance in the Assessment of Target
Bacteria.
Table 14. Media Performance in the Assessment of Nomarget
Bacteria. .... 85
Table 15. Specificity and Selectivity of Viable Count Methods In the
Assessment of Influents 8 6
Table 16. Specificity and Selectivity of Viable Count Methods in the
Assessment of Effluents 8 7
Table 17. Average indicator Organism / AODC Ratios per Viable
Count Method Assessed 8 8
Table 18. Bacterial Species Isolated from Influent Waters 89
Table 19. Bacterial Species Isolated from Effluent Waters 90
Table 20. Bacterial Species Isolated for Chlorinated Effluents 9 1
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LIST OF FIGURES
Page No
Figure 1. Study Sites ;.._ 9 2
Figure 2. Total Coliform Densities found in Bayamon Influent
Waters 93
Figure 3. Total Coliform Densities found in Guaynabo Influent
Waters 94
Figure 4. Total Coliform Densities found in Yauco Influent Waters ....95
Figure 5. Total Coliform Densities found in Villalba Influent
Waters 96
Figure 6. Fecal Coliform Densities found in Bayamon Influent
Waters _ .97
Figure 7. Fecal Coliform Densities found in Guaynabo Influent
Waters 98
Figure 8. Fecal Coliform Densities found in Villalba Influent
Waters 99
Figure 9. Fecal Coliform Densities found in Yauco Influent
Waters , 1 0 0
Figure 10. Fecal Streptococci Densid.es found in Villalba
Effluent Waters 1 0 1
Figure 11. Influent AODC at all Sites 102
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EVALUATION OF TOTAL COLIFORM, FECAL COLIFORM, AND
FECAL STREPTOCOCCI AS ADEQUATE INDICATORS IN
MONITORING PUBLIC WATER SUPPLY QUALITY IN THE
TROPICS,
JOSE MARCOS SOTO MUNOZ
ADVISOR: DR. TERRY C HAZEN
ABSTRACT
Previous studies in tropical areas have demonstrated that high
false positive and negative errors were commonplace when determining
whether fecaJ contamination of source water has occurred. Other
studies had shown that microbial pathogens can have higher survival
rates relative to standard indicator bacteria. These violations to the
criteria used to select an ideaJ fecal contamination indicator organism
proposed doubts about the use of standard techniques in determining
the quality of tropical public water supply systems.
To determine if Escherichia coij was an adequate indicator of
tropical drinking water quality, influents and effluents from four water
treatment plants in Puerto Rico were tested weekly for i three month
period. Membrane filtration and multiple rube fermentation techniques
were used to detect total conforms, fecal coliforms, and fecal
streptococci on appropiate selective media. All presumptive positive
results were confirmed according to standard methodology. From each
sample, an appreciate percentage of organisms giving confirmed fecal
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coliform results were isolated and identified. Total bacterial densities
were determined by acridine orange direct counts.
Results show that while plant effluents generally complied with
current federal regulations, high densities of bacteria were present in
drinking water. Among these bacteria, injured (viable but
unculmrabl*) conforms were detected by m-T7 agar. High levels of
false positive results were found for both the raw and drinking water
samples. Turbidity levels apparently affected the disinfection process,
the normal expression of indicator bacteria in standard media, and the
methodologies used to assess water quality. Neither coliforms nor fecal
streptococci were adequate indicators of tropical water quality.
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INTRODUCTION
In recent years, the number of cases of waterborne diseases in
Puerto Rico has increased every y«^r (Puerto Rico Department of Health,
1986). The most probable cause for this, according to the United States
Environmental Protection Agency (EPA), the Environmental Quality
Board (EQB) and the Puerto Rico Acqueduct and Sewer Authority
(PRASA), is the over capacity and antiquity of the island's water
treatment and purification plants, as well as the increased quantities of
raw sewage and other pollutants being discharged into the island's
water supplies (Ruiz de la Mata, 1985). The mixture of pathogenic
bacteria and industrial pollutants, along with the possible
ineffectiveness of the water treatment process represent an increased
risk of waterborne disease epidemics.
The standard techniques being used to assess the microbiological
quality of treated water (coliform bacteria assays) were developed for
use in temperate climates, where the target indicator organism,
Escherichia. coii. is thermotolerant, whereas the environmental bacterial
flora is not. In the tropics, most assumptions about the indicator
organism become invalid since some environmental flora can withstand
the higher temperatures used to recover E. coli. thus resulting in high
numbers of false positives (Santiago- Mercado and Hazen, 1987).
Studies made in the Microbial Ecology Laboratory of The University of
Puerto Rico have shown an increased pathogen survival rate relative to
regrowth of the indicator organism in the environment (L6pez-Torres,
1982; Hazen et al., 1982; Biam6n and Hazen, 1983).
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Accurate determination of the level of biological contamination is
extremely important in tropical areas, since these areas possess larger
numbers of waterborne diseases. This determination is more difficult in
tropical source water than it is in temperate waters due- to several
factors. Among the physico-chemical variables that affect accurate
determinations of biological pollution in the tropics, we find
temperature, high organic content, high light intensity, and heavy
rainfall (Hazen, 1988).
In temperate climates, water temperature ranges from freezing to
30°C, while tropical water temperature may be as high as 45° and never
goes below 15°C (Hill and Rai, 1982). In the tropical forests of Puerto
Rico, water temperature ranged between 18° and 24CC throughout the
year (Carrillo et ah, 1985; Ldpez et ah, 1987). This thermal environment
results in permanently stratified (oligomictic) water reservoirs as well
as lower amounts of dissolved oxygen relative to temperate waters
(Hutchinson, 1977). In Puerto Rico, large diurnal variations (1 to 8
mg/L) in the dissolved oxygen content of water have been measured
(Ldpez-Torres et al., 1987; Carrillo et al., 1985; Perez-Rosas and Hazen,
1988). This indicates that tropical source waters become anoxic faster
than those of temperate climates, and thus, few organisms apart from
mesophilic, facultatively anaerobic bacteria can thrive in the
environment (Hazen, 1988; Carrillo et al., 1985).
Light intensity in the tropics can adversely affect our perception
of the levels of indicator bacteria present in tropical source water. The
constantly high light intensity, along with high temperatures, cause
constant hypereutrophy of tropical surface waters. The higher densities
of naturally occurring bacteria in tropical waters can limit the reliability
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of assays that employ viable counts to determine the level of biological
water contamination. Tropical source waters have a tendency to
overgrow on standard media (Lo'pez-Torres et al., 1987; Samiago-
Mercado and Hazen, 1987). Also, non-indicator (background) bacteria
can produce bacteriocins that inhibit the growth or the typical
appearance of indicator bacteria on standard media (Means and Olson,
1981; Burlingame et al., 1986).
Tropical rainfall also affects dramatically the microbial content of
water. Some watersheds in Puerto Rico may receive more than 10 cm
of rain in 24 h (Carrillo et al., 1985). Heavy rainfall changes the
nutrient and microbial content of water reservoirs by flushing the
surrounding land and vegetation into the reservoir (Carrillo et al., 1985;
Hill and Rai, 1982; Oluwande et al., 1983). The washing of soil into
streams and lakes also increases the turbidity of reservoirs. Studies in
Africa, Puerto Rico, and Hawaii have clearly demonstrated that
increases in total bacterial densities coincide with increasing rainfall
(Barrel and Roland, 1979; Oluwande et al., 1983; Carrillo et al., 1985;
Fujioka and Shizumura, 1985).
When discussing the variables that make tropical water quality so
different from that of temperate water, we must also take into
consideration the fact that the quantity and quality of bacterial
resources are different between these two environments. The
hypereutrophic state of tropicaJ waters, combined with the higher
temperatures, create a high organic content environment dominated by
mesophilic and thermotolerant microbes (Hill and Rai, 1982; Santiago-
Mercado and Hazen, 1987). This microflora is similar to that of the gut
of animals (Hazen, 1988). Tropical waters also have higher numbers of
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both autochtonous and allochtonous microbes (Hill and Rai, 1982). The
combination of the particular -microbial population and environmental
conditions of tropical source waters result in microbial survival and
activity rates that are totally different from those of temperate waters
(Hazen, 1988; McFeters and Stewart, 1972).
In the tropics there are a lot more pathogenic microorganisms
than there are in temperate climate zones. In fact, many tropical
diseases are totally unknown in temperate climates. Therefore,
microorganisms to be used as indicators of tropical water contamination
must cover a wider range and diversity of pathogens. However, tropical
nations have adopted regulations developed for industrialized
temperate nations without testing their validity, or the validity of
techniques used to assess water quality under these regulations, in their
particular circumstances. This often results in these nations not being
able to meet their own water quality standards (Hazen, 1988). This
problem arises in part from a number of social and economic factors.
Although 65% of the world's population lives in the tropics, these people
actually have less than 10% of the the world's wealth (Odum, 1971).
Since resources are limited, funding for the costly research needed to
find better indicators of water pollution are virtually non-existent.
False estimates of contamination can be avoided by studying the
methods currently being used for water analysis in Puerto Rico as well
as the environmental flora present in source water and in finished
drinking water. Studies of this nature would reduce the possibilities of
waterborne disease epidemics by allowing us to understand better what
bacterial contamination indicators are telling us. Results of this study
-------
could be used to modify existing regulations regarding the bacterial
quality of finished drinking water in tropical climates.
STATEMENT OF PROBLEM:
Tropical waters have been identified as carriers of a large number
of pathogenic microorganisms. In Puerto Rico, water reservoirs receive
large quantities of industrial, agricultural, and domestic sewage
effluents which, when combined with the potentially pathogenic
microbes in the environment, represent an increased risk of waterborne
disease outbreaks. The techniques commonly used to examine the
microbiological quality of drinking water rely on assumptions made
about the target indicator organism (Escherichia coin, especially
thermotolerance (Bonde, 1977). These assumptions may be invalid in
tropical climates. Regrowth of the indicator organism, as well as lower
pathogen survival rates have been observed in tropical areas (Carrillo et
al., 1985; Lopez-Torres et al., 1987; Hagler et al., 1986; Fujioka et al.,
1981; Lavoie, 1983; Hazen et al., 1982; Hazen et al., 1987; Santiago-
Mercado and Hazen, 1987).
Water quality in Puerto Rico is a major problem. Various types of
effluent, such as domestic and municipal sewage, industrial effluents
and agricultural effluents and run-offs, are being discharged into most
of the island's rivers and coastal areas. In 1978, the United States
Water Resources Council reported that %% of ail samples collected from
24 sampling stations throughout the island violated the total coliform
standards. The United Slates Geological Survey has since reported that
80.6% of all water sampling stations in Puerto Rico violated the
recommended maximum contaminant levels for recreational waters
(<1000 fecal coliforms / 100 ml) (Curtis et al., 1984). As contamination
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of public water supplies increases, so has the number of cases of
gastroenteritis (possibly waterborne) (Puerto Rico Department of Health,
1985). In 1976, improper chlorination of the drinking water supply in
the town of Comerfo resulted in 7,800 cases of gastroenteritis (U. S.
Water Resources Council, 1978). During the summer of 1987, an
outbreak of gastroenteritis and dysentery was documented in the town
of Yauco (Puerto Rico Department of Health, 1987). The cause of the
outbreak was linked to the drinking water supply from the water
purification plant at Ranchera Ward, and, in fact, once the plant was
cleaned and water flow returned to normal, the outbreak subsided
(Puero Rico Department of Health, 1987; EPA, 1987). Deteriorating
water quality may be the source of the marked increase in cases of
gastroenteritis, dysentery, and other undiagnosed diseases being
reported by The Puerto Rico Department of Health (from 29,455 cases in
1984 to approximately 73,722 in 1988).
Many common bacteria of water, such as Klebsiella pneumoniae.
Aeromonas hvdrophila. Legionella pneumophila. Pseudomonas spp.,
Vibrio cholerae. Vibrio vulnificus. and Vibrio parahaemoiyticus. as well
as their indicator, Escherichia coli. are potential pathogens of man
(Ha-zen et al., 1987). Many of these organisms can be isolated from
polluted and unpolluted waters in Puerto Rico, although not all of them
have been linked to waterborae disease occurrence (Hazen et al., 1987).
Almost aJl pathogens are transmitted to water by fecal contamination.
For this reason, bacteria found exclusively and universally in feces are
assayed as indicators of possible pathogens in the water. Coliform
bacteria assays are the most practical, since assaying for the presence of
pathogens themselves is costly, difficult, and time consuming. The
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pathogens may be in extremely low quantities in the water (v,hich
males it difficult to isolate and identify them) and still be infective.
Coliform bacteria have been traditionally used as indicators of
possible fecal contamination, since they were thought to be present only
when feces had been deposited in the water (Hutchinson and Ridgway,
1977). Conforms are subdivided into total and fecal coliforms. All are
gram negative, non-spore forming, aerobic or facultatively anaerobic
bacilli that ferment lactose at 35°C, producing gas (Sergey's Manual,
1974). More than 50 bacterial species have positive coliform reactions
and can be found in the feces of warm blooded animals. Fecal coliforms
also possess a characteristic higher thermal tolerance; that is, the ability
to ferment lactose at 44.5°C. This characteristic allows the isolation of
the group through the use of elevated temperature tests. IL. coli.
Citrobacter freundii. Enterobacter cloacae, and Klebsiella pneumoniae
are members of this group (Bergey's Manual, 1974). Assays for fecal
coliforms usually focus on Escherichia c,oU. first described in 1855 by
Escherich. £.. coli is found in high densities in the feces of warm blooded
animals. This bacterium is the major component of both the total and
fecal coliform group (Lavoie, 1983), and is nearly always found, in fecal
contaminated water. However, other coliforms may be found even
when £.. colj is absent. £.. coli is also the coliform most affected by water
treatment procedures (APHA, 1985). Among the most common
coliforms found in both raw and treated water supplies, £.nte_robacte.i
species are the most prevalent. Other genera of common occurrence,
are Citrobacter and Klebsiella.
The underlying principles governing good indicator bacteria (as
stated bv Bonde in 1977) are:
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1. Indicator bacteria must be present whenever pathogens are
present.
2. Indicator bacteria must be present only when the pathogens
are present.
3. Indicator bacteria must be significantly more abundant than
pathogens.
4. Indicator bacteria must resist both the environment and
desinfectants better than pathogens.
5. Indicator bacteria must grow easily on relatively simple media.
6. Indicator bacteria must yield characteristic reactions so they
can be unambiguously identified.
7. Indicator bacteria must be randomly distributed in the water.
8. Indicator bacteria must grow independently of other organisms
on artificial media.
The problem of using temperate water quality standards in the
tropics has long been a source of discussion. In a study conducted by
Feachem (1974) on the waters of New Guinea, fecal coliforms (FC) and
fecal streptococci (FS) levels were greater than 100 CFU/100 ml,
rendering all sites unacceptable as drinking water sources. He
concluded these sites were grossly contaminated with feces. Yet FC and
FS densities were lowest in areas with high human population. Evison
and James (1975) reviewed literature from various tropical areas and
concluded that E.. coli densities at these areas did not correlate to
sources of fecal contamination. Several other studies have
demonstrated that supposedly universal indicators of fecal
contamination were inappropriate for use as such in the tropics. These
include studies showing no correlation between the indicator organisms
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and the presence of Sa 1 mone 1 La. sp. in v,ater (Wright, 1982a; Thomson,
1981); studies reporting high coliform and streptococci densities in non-
contaminated streams (Fujioka and Shizumura, 1985); studies reporting
the occurrence of alarmingly high coliform densities upstream from
sewage contamination sites (Oluwande et al., 1983); and studies
confirming the low accuracy of standard media for the isolation of £,.
coli. from tropical source water (Lavoie, 1983).
In temperate waters, £.. coli fits most of Bonde's principles. The
assaying methods for this particular bacterium take advantage of its
thermotolerance as opposed to the lack of thermal tolerance of the
environmental bacterial flora to isolate it in growth media. In the
tropics, this could be invalid, since components of the normal
environmental flora can also be thermotolerant. Enteric organisms,
being adapted to the high temperatures of the gut of warm blooded
animals (usually around 37°C), can survive and grow in tropical waters
(Bigger, 1937; Ragavachari and Iyer, 1939), especially since these
waters are high in nutrient concentrations (Hazen et al,, 1982; Hazen
and Aranda, 1981). Studies have shown that IL ££>U can persist in
tropical freshwaters (Carrillo et al., 1985; Biam<5n and Hazen, 1-983;
Perez-Rosas and Hazen, 1988; Hazen et al., 1982). In fact, E. coli seems
to be a part of the normal aquatic flora of the tropics (Ha^en et al., 1987;
Bermudez and Hazen, 1988; Fujioka and Shizumura, 1985). Few studies
have been done on the activity, survival, and density of coliforms in
tropical environments (Hazen et al., 1982; Biam6n and Hazen, 1983;
L6pez-Torres, 1982; Ortiz-Roque and Hazen, 1983; Fuentes et al., 1983;
Carrillo et al., 1985; Pe*le et al., 1981). It has also been shown that
many coliforms can subsist as free living saprophytes (Bergey's Manual,
-------
0
1974), and, as such, can multiply and form slimes inside water
distnbution pipes. The isolation of high densities of the same group at
one site may be indicative of such growth (APHA, 1985).
The findings by The U. S. Water Resources Council~and The U. S.
Geological Survey (where most sites sampled by both violated the
maximum contaminant levels for conforms) resulted in the
condemnation of source water in Puerto Rico. Yet recent studies
(Carrillo et al., 1985; Hazen and Aranda, 1981; Lopez-Torres et ah, 1987,
Sandago-Mercado and Hazen, 1987, Rivera et al., 1988; Bermudez and
Hazen, 1988) indicate that even water from pristine sites in the
Caribbean National Forest exceed the maximum contaminant levels for
fecal coliforms, yet less than 40% of 300 fecal coliform isolates were
actually £. coli (Santiago-Mercado and Hazen, 1987). Similar studies
involving the same methods in temperate climate zones (Pagel et al.,
1982) demonstrated that 90% of all fecal coliform isolates were £.. coli.
This reveals that the environmental flora in Puerto Rico has bacteria
capable of producing false positive reactions when assaying for the
presence of fecal coliforms. Comparisons of four fecal coliform assay
methods in Canada and Puerto Rico yielded interesting results: The
specificity (ability of the medium to restrict growth of organisms other
than the target bacterium) of the media in Puerto Rico was significantly
lower (at least by 20%) than that recorded by Canadian investigators
(Pagel et al., 1982). It seems that, in Puerto Rico, all methods have
significantly higher false negative errors, even though the accuracy of
methods was the same on the island as it was in Canada. Santiago-
Mercado and Hazen (1987) showed that high densities of thermophilic
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1 1
and mesophilic background flora in tropical waters significantly reduced
the levels of £., coli on standard coliform media.
In addition, it has been shown that, in Puerto Rico, many
pathogens can be found in the complete absence of £. coli. These
include Candida albicans (ValdeVCollazo et al., 1987), Klebsiella
pneumoniae (L6pez et al., 1987), Legionella pneumophila (Ortiz-Roque
and Hazen, 1987), Vibrio cholerae (Perez-Rosas and Hazen, 1988),
Yersinia enterocolitica (Eh'as et al., 1988), and Aeromonas hvdrophila
(Hazen et al., 1981). Under these circumstances, results from recent
studies on the culturability of certain pathogens become frightening.
Studies by Xu et al (1982), Colwell et al (1985), and Roszak et al (1984)
have shown that E. coli. as well as pathogens such as Salmonella
enteriditis, and Vibrio cholerae may survive and remain pathogenic in
the environment, but are unculturable on standard media. Furthermore,
the presence of enteric viruses in tropical source water has also been
documented in the absence of E_. coli (Berg and Metcalf, 1978; Toranzos
and Gerba, 1988; Keswick et al., 1985; Rose et al., 1975; Cabelli, 1983;
Hejkal et al., 1982), yet viral assays are rarely performed.
The survival characteristics of £.. coli in the tropics also affect its
performance as an indicator organism in these environments. In 1937,
Bigger was the first to report the growth of coliforms in tropical water.
Then, in 1939. Ragavachari and Iyer demonstrated the survival of
coliforms for several months in tropical river waters. Recent studies in
Puerto Rico (Carrillo et al., 1985; L^pez-Torres et al,, 1987; ValdeV
Collazo et al., 1987; Hazen et al., 1987) have demonstrated the survival
and proliferation of £L coli in tropical rain forest streams. Studies have
also demonstrated that under the conditions prevailing in a tropical rain
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forest, Candida albicans (Valdes-Collazo et al., 1987), Klebsiella
pneumonia? (Lop-ez-Torres et al., 1987), Legionella pneumophila (Ortiz-
Roque and Hazen, 1987), Vibrio cholerae (Pe"rez-Rosas and Hazen, 1988),
Yersinia enterocolitica (Elfas et al., 1988), Aeromonas rfydrophila
(Hazen et al., 1981), and Salmonella typhimurium (Jimenez et al,, In
Press) have survival rates different from those of their indicator, £.. coli.
Differences in survival rates between £., coli and pathogens in tropical
source waters hamper its ability to indicate the presence of pathogens
in these waters. Escherichia coli always survives much longer in situ in
tropical waters compared to temperate waters. The increased survival
rates further stress the fact that temperate drinking water regulations
are unapplicable to tropical waters (Hazen, 1988).
Escherichia coli has been found on epyphytic vegetation 15 rn
above the ground in a Puerto Rican rain forest (Rivera et al., 1988;
Bermudez and Hazen, 1988). This points to the possibility of naturally
occurring E_. coli strains in some tropical environments. These
environmental isolates have biochemical and physiological
characteristics, plasmid profiles, coliphage susceptibilities, and antibiotic
i
sensitivities that are very similar to those of clinical E_. coli isolates
(Rivera et ah, 1988). In fact, the environmental isolates were found to
have more than 85% DNA homology and identical mol% G+C with £.. c_oJi
B. Naturally occurring £. coll could account for the results of studies
showing high coliform densities in the absence of a fecal contamination
source. This finding is yet another reason why, in the tropics, E_. £olj
does not fit the underlying assumptions of a good indicator organism.
The above mentioned studies, plus the fact that coliforms have
been shown to be less resistant to chlorination than some human
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1 3
pathogens (Berg and Metcalf, 1978), indicate that the criteria used for
determining the bacterial quality of drinking water in Puerto Rico may
be incorrect; that is, fecal contamination may be detected when in fact,
there is none. With this in mind, it is not surprising that we find it
difficult to meet the legislated standard for drinking water as stated in
the Safe Drinking Water Act (Public Law 93-523, 1974; modified in
1986 to meet changes proposed by the U.S. E..P.A. in 1983). Two
possibilities remain: either to change the indicator system and / or the
maximum contaminant levels used, or to enumerate pathogens directly.
Studies suggest that fecal streptococci (enterococci), enumerated with
selective media, may be better indicators of fecaJ contamination than
fecal coliforms (Hazea, unpublished data).
Enterocccci are a subgroup of the fecal streptococci. The group is
composed of Streptococcus faecalis. S^ faecalis subsp. liquefaciens. $_L
faecalis subsp. zymogenes and £L faecium. These organisms are gram
positive, non-spore formers that have a spherical shape and are
arranged in pairs or chains (Bergey's Manual, 1974). They have been
used as secondary indicators of water quaiity, but the problem with
coliforms has renewed interest in them as primary indicators of water
pollution (Dutka and Kwan, 1978; Cabelli, 1983). Fecal Streptococci have
not been observed to regrow in natural waters (Evison and James,
1975). They have short survival times outside their natural habitat, so
all detected pollution must be recent (APHA, 1985). Streptococcus
faecalis is commonly found in human feces (Bergey's Manual, 1974).
Other fecal streptococcus species, such as S. bovis, and S^e.quinui are
found in feces of warm blooded animals (Bergey's Manual, 1974).
Enterococci have been proposed as indicators of human fecal
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contamination (Berg and Metcalf, 1978; Cabelli, 1983; Cabelli et a].,
1983). Fecal Streptococci densities correlate better than those of E. coli
and total coliforms for the presence of enteric viruses (Berg and Metcalf,
1978; Cabelli, 1983).
Media used to enumerate enterococci, such as KF agar, contain
sodium azide, which interferes with cytochrome oxidase in the electron
transport chain of aerobic organisms, limiting unwanted growth
(McFaddin, 1980). This is an important fact in the avoidance of over or
underestimation of actual bacterial densities. It has been suggested
that the densities of fecal coliforms and enterococci can be compared to
determine the origin of contamination. Ratios of 4.0 or above indicate
human fecal contamination, whereas ratios of 7.0 or less indicate animal
fecal contamination (Geldreich. and Kenner, 1969). The fecal coliforrn /
enterococci ratio has been criticized because of differences observed in
the survival rates of enterococci and fecal coliforms and because of
differences found among individuals with differing diets. Hill et al.
(1971) found that individuals with vegetarian diets exhibited greater
densities of enterococci in their bacterial flora. Later, it was found that
in environmental water above 20°C, £_,. coli survived longer than
enterococci (Evison and James, 1975). These findings suggest that the
fecal coliforrn / enterococci ratios can vary, affecting our perception of
the actual source of contamination as well as the extent of pollution.
The method has been used; however, to examine the quality of waters
while trying to determine whether pollution was due to human fecal
contamination or to storm waters (Feachem, 1976; Wheater et al., 1979).
This study will examine the presence and densities of total and
fecal coliforms, as well as enterococci, as indicators of fecal
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contamination of drinking water and different methods of enumeration
of these bacteria in order to establish whether they are adequate
indicators of bacterial pollution in tropical treated drinking water.
Samples will be taken from raw water at various purification plants of
the PRASA system (influent: water coming directly from the natural
reservoirs) as well as finished drinking water processed in the plant
(effluent). The study will determine if water treatment affects the
levels; of coliforms or enterococci present in finished drinking water
and whether these organisms are good indicators of recent fecal
contamination of tropical source waters. The objectives of this study
are:
1. To determine which indicators best point to recent fecal
contamination of tropical drinking water.
2. To determine which organisms are affected by treatment at
potable water purification plants and whether treatment
affects the levels of fecal coliforms.
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MATERIALS AND METHODS
Study Sites. Four filtration plants operated by PRASA were
selected for the study:
Rancheras Water System, located on road 371, Km 13.0 in the
town of Yauco was selected for this study since it was the source of the
etiologicaJ agent of a gastroenteritis outbreak that occurred during the
summer of 1987. This treatment plant, with a processing capacity of
1,368,000 liters of water per day (GPD), serves a population of 1,440
individuals. The raw water, obtained from Duey River, does not receive
aeration. The pH is adjusted through the addition of lime (CaO).
Aluminum sulfate (Al2(S04)3) is added for flocculation purposes. Algae
are controlled during periods of slow flow by the addition of copper
sulfate. Records of the addition of these chemicals are not available.
Raw water is prechlorinated at the water inlet using chlorine gas at a
rate of 0.08 kg/24 h. The water is then deposited in a sedimentation
tank for 1 hour and then filtered by gravity through a rapid sand filter.
Post chlorination occurs at the pipe leading to the storage tank at a rate
of 0.2 kg/24 h. The plant has storage facilities for 57,000 liters of
finished water. This plant has never had problems with odor or color of
finished water.
The Villalba Urban System, located on road 513, krn 1.2, receives
water from four sources: El Guineo Lake, which has sediments that
affect the raw water quality (particularly the odor and taste); Aceituna
Lake, which receives water from El Guineo La.ke through a concrete
channel; Jacaguas River, which is probably the main source of
contamination, since there is a sewage plant discharge line about 4
-------
miles upstream from the water intake line; and El Semil Dam, providing
spring water coming through rock and discharged into a concrete
channel that is not protected against grazing livestock (another possible
contamination source). This plant was selected for the study because of
its location relative to the sewage processing plant. Since a water intake
is located downstream from a sewage discharge line, the raw water at
the plant contains high levels of fecal contaminants. The water is
sedlmented for one hour, filtered through a rapid sand filter, and
chlorinated by adding gas until an average concentration of 2.5 mg/L is
achieved. Finished water is stored in 8 tanks having a total capacity of
2,698,000 liters.
Guaynabo Water Treatment Plant, located on road 873, Barrio
Frailes Altos in Guaynabo, serves the town of Caguas and some sectors
of Guaynabo. Raw water comes from tanks in Aguas Buenas, which in
turn receive water from Cidra Lake. This plant was selected because of
the contamination levels at Cidra Lake due to illegal sewage discharges
as well as by the distance that the water has to travel before it reaches
the plant (approximately 15 miles). Both of these factors might affect
the raw water quality. Water processing is carried as previously
described: no aereadon is necessary, CaO and Al2(864)3 aje added,
sedimentation occurs over one hour, and water is filtered through rapid
sand filter beds. Chlorination is achieved by the addition of chlorine gas
until an average final concentration of 1.4 mg/L is achieved in the
water. Finished water is stored in 4 uncovered tanks with capacity for
about 133,000 liters each.
La Plata System, located on road 872, km 5.2 in Toa Alia, serves
various sectors of Toa Aha and Bavamon. This svstem receives water
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directly from La Plata Dam, which in turn receives water from La Plata
River. This river receives sewage discharges upstream from the dam
and is also used for primary recreation (possible pollution sources).
Water is treated with aluminum sulfate to form floes, sedimented for
one hour, pre-chlorinated (at 0.1 kg/24 h), filtered through rapid sand
filters, and post-chlorinated using gas until the chlorine content of the
waters reaches an average of 1 mg/L. Water is also aereated. In
addition to lime and aluminum sulfate, polyphosphates (for the control.
of corrosion), and fluoride are added to the water. Finished water is
stored in a subterranean tank with a capacity of 19 million liters.
Sampling. Water was grab sampled into sterile, one-liter Nalgene
screw-cap bottles at the water intake and at the finished water primary
distribution line. Samples from the finished water line were collected in
the same type of container with an appropiate volume of a sterile 1%
aqueous solution of sodium thiosulfate added to neutralize free chlorine.
All samples were kept according to sample custody procedures
described in Standard Methods for the Examination of Water and
Waste water (APHA,1985) and transported to the laboratory within 5 h
of collection.
Water Quality. Water temperatures were measured in situ using a
standard, centigrade scale thermometer. Total and free chlorine levels
were also measured in situ with a Hach chlorine test kit employing the
color comparator technique (Model CN-66, Hach Co., Loveland, Colorado).
The pH was measured upon collection using a portable pH meter.
Additional, small volume water samples were collected into sterile
Whirl-Pak bags (Nasco, Ft. Wilkinson, Wisconsin) and transported to the
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1 9
laboratory for turbidity analysis using a Model 16SOO Turbidimeter
•> 1 / W
(Hach).
Bacterial Analysis;
Total Direct Counts. Bacterial cell densities and the percentage of
those active in protein synthesis were determined by acridine orange
direct counts (AODC). For AODC, an appropiate volume of the sample
was filtered through 47 mm diameter, 0.22 jam pore size, polycarbonate
membranes pre-stained with 0.07% irgalan black (Nuclepore,
Pleasantville, California). After filtration, 2 ml of acridine orange "(0.1%,
aqueous solution) was added and left on the filter for 2 minutes,
filtered, and the excess washed off with sterile water. Stained filters
were observed using the 100X objective of an epifluorescence
microscope (Model 2071, American Optical Corp., Buffalo, New York).
Total direct counts were determined by the average of the number of
fluorescing bacteria in ten randomly selected fields on the membrane
multiplied by 98177.77 (the number of fields on the filter). Acridine
orange binds to nucleic acids and fluoresces red or green under the
epifluorescent microscope depending on the amounts of RNA and DNA
present in the cells (Hobbie et al., 1977).
Percent Activity. The percentage of cells active in protein
synthesis were calculated indirectly from the total acridine orange
direct counts. The ratio of red fluorescent cells (having greater amounts
of RNA) to the total number of fluorescent cells as obtaine-d in the
acridine orange direct counts represents the proportion of cells actively
transcribing their genetic code in order to produce proteins (Hobbie et
al., 1977).
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20
Membrane Filtration Studies, Levels of total and fecal coliforms,
as well as fecal streptococci were measured by membrane filtration
studies according to Standard Methods (APHAJ985). Volumes of 1, 10,
100, and 1000 ml were used in each case. All presumptive growth was
confirmed by inoculating at least 10% of the colonies on each plate Into
appropiate Standard Methods (APHA.1985) media as follows:
Colonies presenting the typical green metallic sheen on m-Endo
agar (Difeo Laboratories, Detroit, MI) after 28h incubation at 37°C were
confirmed according to their ability to grow and ferment lactose with
the production of gas within 24 h on Brilliant Green Lactose Bile (2%)
broth and Lauryl Tryptose broth at 37°C (APHA,1985). Colonies giving
positive reaction on both media were subcultured on McConkey agar for
24 h at 37°C to verify their identity as lactose positive, gram-negative
organisms (APHA,1985). All media were purchased from Difco
Laboratories.
Fecal coliforms present a typical blue color on m-FC agar (Difco)
after 24 h incubation at 45°C, while all other colonies are gray. m-FC
agar is prepared according to the formulation published by Geldreich et
al (1969) and supplemented with 10 ml of 1% rosolic acid in 0.2'N NaOH
solution per liter. Presumptive fecal coliforms were confirmed on EC
broth (Difco), which contains 0.15% Bacto Bile Salts No. 3. These bile
salts inhibit spore formers and fecal streptococci from growing while
enhancing the growth of E_. coll at 45°C (Hajna and Perry, 1943).
Organisms positive for the production of gas on EC broth at 45°C after
24 h were streaked on McConkey Agar (Difco) and incubated at the
same conditions to isolate lactose positive, gram negative colonies.
Approximately 10% of these organisms per site per collection were
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identified using the API-20E system for the identification of
enterobacteriaceae and other gram negative bacteria (Analytab
Products, Inc., Plainview, New York).
KF Streptococcus agar (Difco) was used to assay for the presence of
fecal streptococci. This medium contains sodium azide, which interferes
with the electron transport chain of aerobic organisms (Kenner et aL,
1961). The addition of 1 ml triphenyltetrazolium chloride 1% per 100
ml of medium results in a red color of the colonies for easy
identification. Color is produced as enterococci reduce the tetrazolium
to an acid dye (Difco Laboratories, 1984). Presumptive enterococci after
incubation of KF Agar at 37°C for 48 h were confirmed on Azide
Dextrose broth (Difco) incubated under the same conditions. Azide
Dextrose broth has the same selective agent as KF Agar. Turbidity of
the medium after 48 h was considered as positive for the presence of
streptococci. The broth was then streaked on m-Enterococcus agar
(Difco), which does not recover such fecal streptococci as 5.. bovis and £.
equinus (thus selecting streptococci of human fecal origin). This
medium was found to be 100% selective when assaying heavily polluted
waters (Difco Laboratories, 1984). Reddish colonies on this medium
after 48 hours at 37°C were considered positive for the presence of fecal
streptococci in the samples.
Injured fecal coliforms were assayed using m-T7 agar (Difco), a
new selective medium that allows for the improved recuperation of
these organisms from drinking water. In laboratory studies, this
medium was able to recover 86 to 99% more injured coliforms than m-
Endo agar (LeChevallier et al., 1983). m-T7 Agar recovered nearly
three times more coliforms than did m-Endo agar from drinking water;
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and less than 0.5% of all m-T7 Agar colonies gave false negative
reactions (LeChevallier et al., 1983). Additional selectivity may be
obtained through the aseptical addition of 0.1 jig of penicillin G per ml
of medium after autoclaving (LeChevallier et al., 1983). ~ The identity of
the typical yellow m-T7 coliform colonies obtained after incubating for
24 h at 45 °C was confirmed as described previously for fecal coliforms
detected by m-FC agar.
Most Probable Number. The most probable numbers of total
coliforms, fecal coliforms and fecal streptococci were determined using
the five tube method for 0.1, 1.0, and 10.0 ml water samples and
appropiate selective media (Lactose broth for total coliforms; EC broth
for fecal coliforms; and Azide Dextrose broth for fecal streptococci), as
described in Standard Methods for the Examination of Water and
Wastewater (APHA,1985). All media was purchased from Difco
Laboratories, Detroit, Michigan.
For total coliform bacteria, five tubes of sterile lactose broth were
inoculated per dilution factor (i.e., 5 tubes with 10 ml water, 5 with 1
ml, 5 with 0.1 ml). The procedure was performed for both influent and
effluent. The number of tubes from each dilution showing gas
formation at 37°C after 48 h was recorded and the number of coliforms
looked up on a MPN table in Standard Methods for the Examination of
Water and Wastewater (APHA,1985). Positive tubes from the lowest
dilution were confirmed in the same manner as colonies from m-Endo
Agai (Membrane filtration studies). Fecal coliform bacteria MPN
determinations were performed in the same manner, but the tubes
were incubated at 45°C. Positive tubes from the lowest dilution were
confirmed exactly like the colonies from m-FC and m-T7 agais.
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The MPN technique to assay for fecal streptococci was also
performed with five tubes per dilution factor and at 37 °C.
Confirmation was carried out as with colonies isolated on KF agar.
Quality control. All media were prepared according to package
directions. Unused m-Endo and m-FC agar plates were discarded after 2
days. Unused KF and m-T7 Agar plates were discarded after 30 days.
Unused liquid media tubes were discarded after 90 days. All media
were kept in a dark cold room at 4°C
Sterility of all media was checked for each batch by incubating a
sterile plate or tube at 37° or 45°C, depending on the media. Specificity
of all media was checked for each batch of medium by inoculating one
tube or plate with E_. coli B (ATCC 23848) and another with
Streptococcus faecalis (CBSC 15-5600A) and incubating under the
desired experimental conditions.
Media Performances The relative performances of the viable
count methods were calculated for each site. Analyses of variance were
performed on the average recovery efficiencies of each method. If a
site revealed significant differences, a Student Newman-Keuls test was
performed on the data to determine the nature of the differences.
Results from these tests allowed the rating of methods according to
their performance.
Specificity and Selectivity of the Media: Specificity was evaluated
by confirming 10% of all positive (presumptive target) and negative
(presumptive nontarget) colonies from plates containing 10 to 100 CPU.
The false positive error (FPE) was defined as the number of false
positive target colonies divided by the total number of presumptive
target colonies. False negative error was defined as the number of false
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negative target colonies divided by the number of verified negati\e
target colonies plus false negative target colonies. All results were
expressed as percentages. The procedure was repeated for both
untreated influents and chlorinated effluents.
The selectivity index is both a measure of the inhibitory effect of
background on target bacteria and a measure of the capability of the
media to prevent background growth (Pagel et ah, 1982; Clark, 1980;
Ray and Specks, 1973; Franzblau et ah, 1984). If low selectivity indexes
are found, background bacteria are inhibiting the growth of indicator
bacteria (Clark, 1980). This parameter was calculated by dividing the
number of presumptive target colonies by the total number of isolates
from each media. All results were expressed as percentages.
Data Analysis: Statistical analysis will be performed with
programs developed for Apple Macintosh Plus and Macintosh SE
computers. Multiple correlation and regression analyses to correlate
bacteriological and physico-chemical data were performed using
Statview 512+ software from Abacus Concepts. Analyses of variance
and Takey's tests to determine differences between sites, bacterial
densities, and physico-chemical parameters were performed using TCH-
Stats-ANOVA software developed by Dr. Terry C. Hazen. The log (X + l)
transformation was used to make heteroscedastic data more
homoscedastic. This transformation was selected due to the exponential
variability of the data. Any statistical probability equal to or less than
0.05 was considered significant (Zar, 1984).
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RESULTS
Water quality. Averages of water quality parameters at .each site
are presented on Table 1. These measurements were subjected to
analysis of variance (ANOVA). If significant differences were detected
by site, a Tukey's Test was performed on the data to determine which
sites were different. Measurements of the total and free chlorine
content of influents were not significantly different by site.
Temperature, pH, and turbidity, however, differed by site (Table 1,
Appendix I). Influent water temperature was equal only between
Bayamon and Guaynabo (Tukey's Test: Q=4.23, df=40, P>0.05), while
Villalba and Yauco had significantly lower values. Influent water pH
was significantly lower at Guaynabo (Q=3.71, df=40, P<0.05), while
equal at the other three sites. Influent turbidity was significantly
higher at Yauco, but equal at Bayam6n, Guaynabo and Villalba (Q=8.54,
df=40, P<0.01).
Effluent pH did not differ significantly by site (Table 1, Appendix
II). Effiuent temperature, turbidity, and chlorine content was
significantly different by site (Table 1, Appendix II).
ANOVA between influents and effluents by site revealed a
significant difference in temperatures at Yauco (F=10.56, df=l and 20,
P<0.01). Significant differences were also found in the total and free
chlorine content (F=55.5, 132.2, 133.4, 231.3; df=l and 20; P«0.001; and
F=40.9, 162.4, 229.3, 202.2; df=l and 20; P«0.001, respectively)
between influents and effluents at all sites.
Bacteriological methods. Average bacterial densities at each site
are presented on Table 2. ANOVA of the viable count methods used
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26
revealed no significant differences between m-T7 agar and m-KF agar
influent results by site (Table 2, Appendix III). In Bayamon,
significantly lower densities of total coliforms, fecal conforms, and fecal
streptococci were detected with MPN methods than in any of the three
other sites (Q=7.4, 6.8, 5.5; df=40; P<0.001; see Figs. 2 and 6). None of
the methods used was significantly different by site when assessing
influents (Appendix IV).
ANOVA between methods used to detect the same group of
organisms in influent waters revealed that total coliform densities were
significantly different between membrane filtration and MPN (F=4.28,
df=l,86, P<0.05; see Figs. 2-5). Fecal coliform detection methods were
also different (Figs. 6-9). While no significant difference was found
between m-FC agar and fecal coliform MPN, membrane filtration with
m-T7 agar produced significantly lower results (Q=6.05 and 5.31,
df=129, P<0.01). None of the other comparable viable count procedures
for total coliforms and fecal streptococci showed significant differences
when assaying effluents.
ANOYA of methods pooling sites (Appendix V) revealed, in the
case of influent water, the variability of results from the same method
when used to assess water from different sources. Only m-T7 agar and
m-KF agar gave consistent results between sources of water. In the
case of effluent water, similar tests revealed that for most methods,
water quality from the four plants was consistent (Appendix V).
Notable exceptions were MPN for both total and fecal coliforms; some
plants performed better than others in coliform removal.
AODC and percent cells active in protein synthesis. Average AODC
and percent activity are presented on Table 3. ANOVA of influent AODC
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did not exhibit significant differences by site (F=2.41, df=3 and 41,
P>0.05; Figure 11). Percent activity of the influents was similar only
between Bayam<5n and Villalba, while those of Yauco and Guaynabo
were significantly lower, as revealed by Tukey's test (Q=4.86 and 7.54;
df=40; P<0.05 and 0.01, respectively). Effluent AODC was significantly
lower at Guaynabo (Q=4.62, df=40, P<0.05), while similar at the other
three sites. Effluent bacterial activities were significantly different
between all sites (F= 10.53, df=3 and 40, P«0.001). Further analysis
(Tukey's test) revealed significant similarity between the effluent
activities of Bayamon and Villalba (Q=l,34, df=40, P>0.05), while those
of Guaynabo and Yauco (Q=1.21, df=40, P>0.05) were also similar
between themselves, but lower than the other two.
AODCs were also used as measurements of bacterial background
relative to the indicator organisms assayed. The higher the ratios
between the indicators and AODC, the lower the amount of background
bacteria present in the sample.
Among influents (Table 3), the highest average ratio was obtained
when assaying total coliforms in Guaynabo. In fact, this site's influents
had the lowest overall level of background bacteria relative to the other
sites. On the other hand, Influents from Bayamon contained the highest
levels of background bacteria. In the effluents, the highest indicator /
AODC ratios were those of MPN methods at Guaynabo. MPN
methodology generally had higher ratios than those of membrane
filtration. The lowest ratios were those of Bayamon's effluents.
Correlation of bacterial parameters and water quallty,_Q_f_LQnjjgj]t$
and effluents. Two correlation matrices were performed to determine
the relationship between viable count methods, AODC, the percentage of
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28
cells active in protein synthesis, and water quality parameters of
combined influents and combined effluents. All correlations mentioned
were significant at the P<0.05 level.
In the influents, all viable count methods were positively
correlated with each other (Table 4). In the effluents (Table 5), total
coliforms, as detected by membrane filtration, were positively
correlated with fecal coliforms detected by membrane filtration with m-
FC agar, and total coliform MPN correlated positively with fecal
streptococci MPN.
Correlation of bacterial parameters and water quality at each site.
Two correlation matrixes; one for influent, one for effluent, were
performed by site to determine the relationships between water quality
viable count methods, AODC and percent activity. All correlations
mentioned were significant at the P<0.05 level.
At Bayamon, (Table 6) the influent total coliform levels (as
detected by membrane filtration) correlated positively to all the other
viable count techniques assessed, AODC, and pH. Fecal coliforms
detected by membrane filtration using m-FC agar were positively
correlated to results of fecal coliforms from m-T7 agar, total coliform,
fecal coliform, and fecal streptococci MPN, and pH. Fecal coliforms
detected by m-T7 agar correlated positively with total coliform and
fecal streptococci MPN; There are-positive correlations between total
coliform MPN and fecal streptococci MPN, fecal coliform MPN and fecal
streptococci MPN, and AODC and percent activity. Temperature
correlated positively with total chlorine content, and negatively with
total and fecal coliforms detected by membrane filtration, total coliform,
fecal coliform, and fecal streptococci MPN, and AODC.
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29
In the Bayam6n effluents (Table 7), fecal streptococci levels
determined by membrane filtration were positively correlated with
fecal streptococci MPN, total and free chlorine levels, and fecal coliforms
detected by m-T7 agar. Fecal streptococci MPN were positively
correlated to total chlorine levels. Turbidity was positively correlated
to m-T7 agar fecal coliforms, fecal streptococci (detected by both
membrane filtration and MPN), and free chlorine content. A positive
correlation also existed between total and free chlorine levels.
The Guaynabo influents (Table 7) displayed positive correlations
between membrane filtration total coliforms and membrane filtration
fecal coliforms (using m-FC agar); membrane filtration fecal coliforms
(using m-FC agar) and m-T7 agar, membrane filtration fecal
streptococci, total coliform MPN, and fecal coliform MPN; m-T7 agar and
both fecal coliform and fecal streptococci MPN; total coliform MPN and
fecal streptococci MPN; and fecal coliform MPN and both fecal
streptococci MPN and percent activity. Influent temperature correlated
negatively with pH, m-T7, and all MPN methods. Turbidity was
positively correlated to membrane filtration total coliforms, membrane
filtration fecal streptococci, and AODC. Percent activity correlated
negatively with pH.
In the effluent from the Guaynabo plant (Table 9), membrane
filtration total coliforms were positively correlated to AODC. Also, total
and free chlorine levels were positively correlated.
The correlation matrix for Villalba influent (Table 10) reveals
positive correlations between total coliforms detected by membrane
filtration and fecal coliforms detected by m-FC agar and m-T7 agar,
fecal streptococci detected by membrane filtration, fecal coliform MPN
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30
and fecal streptococci MPN. Fecal coliforms detected by membrane
filtration using both m-FC agar and m-T7 agar were also positively
correlated with fecaJ coliform and fecal streptococci MPN. Fecal coliform
MPN were positively correlated with fecal streptococci MPN, as were
AODC and percent activity. Temperature of influent water was
positively correlated to total coliforms detected by membrane filtration,
fecal coliforms detected by membrane filtration using m-T7 agar, and
fecal streptococci MPN. Influent pH correlated negatively with total
coliforms detected by membrane filtration. In the Villalba effluents
(Table 11), total coliforms detected by membrane filtration correlated
positively with fecal coliforms detected by membrane filtration using
m-FC agar, as did fecal coliform MPN and turbidity. Effluent total and
free chlorine content were also positively correlated. Effluent water
temperature was negatively correlated to pH.
In the influent at Yauco, all the viable count methods for all of the
bacterial groups assessed were positively correlated to one another
(Table 12). Temperature also correlated positively to all methods
except membrane filtration for the detection of fecal coliforms using m-
T7 agar. AODC correlated negatively with the percentage of cells active
in protein synthesis. Other negative correlations existed between
influent pH and membrane filtration fecal coliforms using m-FC agar, as
well as with all MPN methods (total coliforms, fecal coliforms, and fecal
streptococci).
The correlation matrix for the effluent at Yauco (Table 13) showed
positive correlation between total coliform MPN and total coliform
membrane filtration, fecal coliform membrane filtration on both m-FC
agar and m-T7 agar. FecaJ streptococci MPN densities were also
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3 1
positively correlated to all these parameters. Fe-cal streptococci
membrane filtration densities were positively correlated to total
coliform and fecal coliform (m-FC) membrane filtration. Membrane
filtration for the detection of fecal coliforms using m-T7 agar were also
positively correlated with these two parameters. AODC correlated
positively to both total coliform and fecal streptococci MPN, while
correlating negatively with percent activity. Turbidity correlated
negatively with pH. The total and free chlorine content of the effluent
were positively correlated.
Media Performance. Media performance when screening for
target bacteria in the influents was not significantly different at
Guaynabo and Yauco (Table 14). At Bayam6a, membrane filtration on
m-Endo agar was able to recover higher bacterial densities than any of
the other methods. This would be expected, since this group should be
the most abundant of the bacteria being assessed. At Villalba, not only
was m-Endo agar able to recover high levels of coliforms, but MPN
analyses performed better than fecal coliform and fecal streptococci
membrane filtration analyses. Evaluation of media performance
relative to the levels of nontarget bacteria reveal that, at Bayamon, m-
Endo agar performed better than any other method (Table 15). At
Yauco, m-Endo, m-KF, and total coliform MPN performed higher than
the rest of the methods as far as nontarget organism recovery.
Guaynabo and Villalba showed no differences among media.
Selectivity and specificity of the methods when assaying influents
are summarized in Table 16, If we accept 15% as the upper level
permissible for false positives, like Pagel et al (1982) did when
evaluating the performance of fecal coliform membrane filtration
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3 2
methods in a temperate climate zone, none of the methods is acceptable,
no matter how well the media performed. Media selectivity for
influents ranged from 29 to 66% (Table 16). Selectivity of the media in
tropical waters was therefore much lower than those reported by Pagel
et al (1982) in temperate waters.
Performances in the assessment of both target and nontarget
bacteria in chlorinated effluents did not vary among media (Tables 14
and 15). However, false positive and negative errors in effluents were
higher than those of influents, and thus, also unacceptable. Selectivities
in chlorinated effluents ranged from 47 to 79% (Table 17). While still
unacceptable, these selectivities are higher than those of the same
media when assessing influents.
Among the species isolated and identified from influents, £.. coli
was the most frequent (42.7%; Table 18). The media isolating this
bacterium most often was m-T7 agar. MPN methodology recovered the
lowest percentage of E.. coli. Other frequently isolated species were
Enterobactcr sp. (27%) and Citrobacter freundii (21%). Enterobacter sp.
was detected most frequently by m-FC agar, while £.. freundii was
observed more frequently on m-T7 agar.
The species most commonly isolated in chlorinated effluents was
also £. coli (61.1%; Table 19). m-T7 agar isolated this bacterium far
belter. (50%) than m-FC agar (27.3%) or FC MPN (22.7%). The next most
frequently isolated species was PjLejajiQJQjQlLiJ. sp. (16.7%). m-FC agar,
apart from detecting practically all Pjeudomonas sp. encountered
(94.4%), also detected £.. freundii and E.. cloacae..
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33
DISCUSSION
Bacterial densities and media performance are affected in various
ways by a great number of physical and chemical parameters that can
act by themselves, synergistically, or in antagonistic ways (Badge et al.,
1982). Our results confirm this fact, as can be observed in the multiple
correlations between the collected data. The bacterial counts we see are
the effects of multivaiiate events. In some cases, these cannot be
explained by the available data; however, those that can are not only
interesting, but of great relevance to the issue of drinking water quality
in the tropics.
Influent temperature seems to be a very important factor
affecting the levels of bacteria. Specific patterns are evident,
particularly in results for the most probable numbers (MPN) of the
bacteria under study. Almost all of the viable count procedures
performed on the influents of La Plata and Los Filtros filtration plants
(Bayamon and Guaynabo, respectively) correlate negatively with
temperature (Tables 6, 8, 10, and 12). There were no significant
differences between the influent temperatures at these two sites, while
those of Villalba and Yauco were significantly lower. Influent bacterial
levels at Bayamon, as measured by MPN, were significantly lower than
those of the three other plants. All of this leads us to beleive that high
influent temperature depresses the amount of organisms present in raw
water, particularly at Bayam<5n. This agrees with results obtained by
Badge et al (1982) from raw waters in New Delhi. Badge and his
colleagues demonstrated that above a specific maximum temperature
level, rapid microbial growth could not occur due to unrepairable
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34
damage to the cytoplasmic contents. This finding is particularly
important since it has been demonstrated that £.. coli can die very
rapidly with slight temperature increases, while other bacteria, such as
Fseudomonas aeruginosa and Citrobacter freundii, which are
opportunistic pathogens, (both were found in influent samples from the
four study sites; See Table 18) survive and can produce false positives
in culture media (Thomson, 1981).
Temperature is also positively correlated with almost all of the
viable count methods performed on the Villalba and Yauco influents,
whose temperatures were well below those encountered at Bayamon
and Guaynabo. We suggest that this phenomenon is caused by an
increased survival rate of the microorganisms during periods of higher
water temperature at these sites. Our suggestion is backed by the work
of Wright (1982a), who observed and described a similar phenomenon
while working on the survival of fecal microorganisms in the waters of
Sierra Leone, as well as by studies done at our laboratory (Carrillo et al.,
1985; Biamon and Hazen, 1983; Perez-Rosas and Hazen, 1988; Hazen et
al., 1982; Hazen et al., 1987; Bermudez and Hazen, 1988; Lopez-Torres,
1982; Ortiz-Rcque and Hazen, 1983; Fuentes et al., 1983; Camllo et al.,
1985; Santiago-Merc ado and Hazen, 1987; Hazen, 1988).
Another factor significantly affecting the microbial content of
water is turbidity. Among the studied influents and effluents, increases
in turbidity bring about increasing total coliform, fecal coliform, fecal
streptococci, and AODC levels, particularly at Guaynabo. Observations
made during samplings point towards rainfall as the cause of increased
influent turbidity. The clay particles and organic matter that make up
turbidity can adsorb ions, molecules, and biological agents, thus
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35
affecting transport and release of toxic materials, bacteria and viruses
(National Academy of Sciences, 1977; Hoff, 1978).
Influents from Guaynabo and Yauco were the most succeptible to
elevated bacterial densities due to increased turbidity -caused by
rainfaJl. The sources of these influents are not protected against
roaming cattle and other domestic animals, or from human activities
such as laundering. We beleive these increased bacterial contents are
caused by the leaching of fecal and other waste materials from river
and lake banks into source water by rainfall. The FC/FS ratios at these
sites under these conditions, point to mixed human-animal fecal
contamination during rainfall events in a pattern is similar to that
reported by Barrel and Rowland (1977) in Western Africa. However,
routine use of FC/FS ratios might be unacceptable under normal
conditions due the the prolonged survival and normal presence of fecai
coliforms in tropical source water. Sediment disturbances caused by
increased water flow is another factor contributing to the elevated
bacterial counts. Riverbed and lake sediments usually have higher
bacterial levels than water due to the adsorption of cells to clay and
other organic and inorganic particles (National Academy of Sciences,
1977). Benthic bacteria washed away by heavy precipitation may also
contribute to the elevated counts, since, in the tropics, these might
include bacteria capable of false positive reactions on standard media
(Hazen, 1988).
A direct correspondence between increasing turbidity of Influents
and effluents was observed. Increases in effluent turbidity (above 1.5
NTU) were also related to increasing bacterial counts at all sites,
although these were statistically non-significant. The statistical
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36
discrepancy must be related to the lack of data points, since the above
mentioned situation occurred a limited number of times at each site.
However, increases in effluent turbidity bring about increased detection
rates of fecaJ streptococci relative to the detection of coliforms.
Detection of bacteria under these circumstances is possible due to the
protection against chlorination that turbidity can provide. As stated
previously, clay, organic, and inorganic particles can adsorb bacterial
cells. These particles also have a tendency to bind together, forming
clumps. Bacteria inside these clumps cannot be effectively affected by
chlorine; instead, they remain sublethally and reversibly injured
(National Academy of Sciences, 1977; McFeters el al., 1986; World
Health Organization, 1987). These injured bacteria remain viable but
unculturable by standard methodology. It has been demonstrated that
the majority of viable bacteria in chlorinated drinking water are
attached to paniculate matter (LeChevallier et al., 1988; World Health
Organization, 1987). Furthermore, the fraction of turbidity composed
by organic material can form compounds with the free chlorine content
of the water, thus inactivating it and permitting the survival of bacterial
cells (National Academy of Sciences, 1977; Goytia, 1978; Hoff, 1978).
This can result in the eventual recovery of bacteria in the water
distribution systems due to increased nutrient availability and
favorable temperatures (McFeters et al., 1986).
In the case of fecal streptococci, we suggest not only the
possibility of bacteria protected against chlorine through turbidity, but
survival rates higher than those of coliforms in the presence of chlorine,
as has been reported with Klebsiella sp. (Ptak et ah, 1973) and some
enteropathogens (LeChevallier and McFeters, 1985), since streptococci
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3 7
were found in greater numbers than coliforms whenever these
occurred. Defense mechanisms against chlorination have recently been
described for several bacteria (LeChevallier et aL, 1988). Further
research is needed to fully understand the behavior of- fecal streptococci
in chlorinated effluents. Correlations between the detected densities of
fecal streptococci, chlorine content, and turbidity (Table 9) confirm
these possibilities. Turbidity values were a highly significant factor
affecting both the chlorine content and fecal streptococci levels at
Bayamon. At the same time, chlorine had a highly significant effect on
fecal streptococci levels. All of these correlations point towards
turbidity as the factor determining the occurrence of fecal streptococci
in drinking water through interference with chlorination. Fecal
coliforms were also detected in Bayamon effluents by m-T7 agar, in a
pattern similar to that of fecal streptococci in terms of the relationship
between bacterial density, turbidity, and chlorine content. The fact that
fecal coliforms detected at this site existed in an injured state (they
were detected only by m-T7 agar) is further evidence of the protection
against chlorination that turbidity particles can confer to bacteria.
Wright (1986) stated that the flushing produced by heavy rainfall
resulted in periods of increased health hazards due to the introduction
of pollutants into bodies of water used as drinking water supplies. Our
observations suggest that heavy rains bring about periods of highly
uncertain water quality. If E^ QoJi is naturally occurring in the tropics, it
would be difficult to determine whether increases in its density during
heavy rainfall axe due to the flushing of strains from fecal material or to
the flushing of strains from the environment into the reservoirs. Heavy
rains also flush soil surrounding the reservoirs and disturb sediments.
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38
The resulting high turbidity affects the efficiency of chlorination and
supports high levels of background bacteria (which interfere with
coliform recovery on standard media). High turbidity also clogs filter
membranes and makes it difficult to properly observe "microbial growth
in fermentation tubes. Special care should be taken when analyzing
potable water samples coming from high turbidity sources, rainy season
or not; particularly if their turbidity is even slightly higher than normal.
We also note that while bacterial counts are increased in both influents
and effluents after the onset of rains, so are the numbers of false
positives and negatives found. We agree with Wright (1986) that
membrane filtration is the method of choice under these circumstances,
since it provides relatively pure colonies that aid in the process of
confirmatory subculturing in appropiate media. However, since high
turbidity can clog filters, special care should be taken in the preparation
of suitable dilutions to overcome this problem. We go further, to
suggest an actual identification of a suitable amount of nontarget
organisms from each fecai coliform negative sample to be absolutely
sure of the absence of fecal contamination of potable water. It is
important to remember that while high turbidity will not necessarily be
an indicator of potential health hazards, low turbidity is not an
inequivocal indicator of the bacterial quality and safety of potable
water.
All of the methods used to monitor the presence of indicator
organisms in tropical source waters seem to be unacceptable due to
their high rates of false positive and negative errors (Tables 16 and 17).
Tropical waters have autochtonous bacteria capable of producing
positive coliform reactions when standard methodology is used
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39
(Santiago-Mercado and Hazen, 1987; Hazen, 1988). These bacteria ca-jse
high numbers of false positives on standard media, which results in
overestimatioo of pollution. The acceptable limit for false negatives
used by Pagel et al (1982) in temperate waters was 5%~. In tropical
waters, we found false negatives ranging from 12 to 50% (Table 16).
Thus, according to specificity indexes, all the methods tested are grossly
unacceptable in tropical source waters. In addition, the high levels of
background bacteria present in tropical waters are capable of producing
metabolic interferences with the normal reactions of indicator
organisms on standard media (Means and Olson, 1981; Burlingame et al.,
1984; Santiago-Mercado and Hazen, 1987; Hazen, 1988). This
interference can result in underestimation of the levels of pollution in
tropical waters. The levels of background bacteria relative to each of
the indicators measured can be determined indirectly from the ratio
between the indicator and the total direct count (determined by AODC).
These ratios range from 0 to 1.08 x 10~4. The indicator bacteria being
assessed by standard media are only 0 to 0.01% of the total microbial
flora present in the analyzed waters (Table 3). The low selectivity
indexes recorded (29 to 66% for influents, 47 to 75% for effluents; see
Tables 16 and 17) relative to the performance of fecal coliform
assessment methods in temperate zones (Pagel et al., 1982) are further
evidence of the inhibitory nature of background flora (Clark, 1980).
Final identification of colonies from fecal coliform methods are yet
another measure of the high density and variability of background
bacteria. Among influents, £.. coli was successfully isolated 42.7% of the
time out of 170 isolates (Table 18). Other common nontarget bacteria in
influents were Enterobacter sp. (15.8%) and Citrobacter freundii (12.3%)".
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40
The Enierobacter and Citrobacter genera have positive fecal coliform
reactions, but are not fecal specific (Bonde, 1977; Hoadley et ah, 1975;
Santiago-Mercado, 1987). Identification of organisms recovered from
chlorinated effluents by fecal coliform methods resulted" in the isolation
of £_. c_pJi 61% of the times out of a total of 43 isolates (Table 19). Other
bacteria frequently isolated from effluents were Pseudomonas sp.
(16.7%) and Enterobacter cloacae (13.9%). The occurrence
of Pseudomonas sp. in chlorinated effluents is important, since this
bacterium, if ingested by humans, might cause various sorts of
infections.
Confirmation rates of both target and nontaiget bacteria (Tables
18 and 19) reveal that a maximum of 61% of all colonies were actually
£.. coll by any fecal coliform method. This is lower than results recorded
by Santiago-Mercado and Hazen (1987), who found that a maximum of
70% of tested colonies were E_. colj in various types of tropical
freshwater. By comparison, Pagel et al (1982) recorded confirmation
rates that were at least 27% higher in contaminated temperate
freshwater. Pagel et al (1982) found high numbers of E_. coii among the
nontarget colonies assayed. She attributed her results to the thermal
stress imposed on the indicator by the colder waters of temperate
climates.
Comparisons of the densities of target fecal coliforms recovered
from influents reveal that m-FC agai had higher recovery rates at
Bayam6n and Guaynabo (Table 14). At Villalba and Yauco, however,
fecal coliform MPN had better recovery rates, as would be expected
from highly polluted waters (Santiago-Mercado and Hazen, 1987). The
high bacterial densities in heavily polluted waters mask fecal coliforms
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4 1
by inhibiting the expression of their normal characteristics. Densities
of total coliforms were higher at all sites when m-Endo agar was used
(Table 14). The highest overall total coliform recovery was recorded on
the VillaJba influent, which is grossly polluted by a sewage outfall.
Influent fecal streptococci were recovered most efficiently by m-
KF at Bayam<5n and Guaynabo (Table 14). At Villalba; however, the
fecal streptococci MPN method performed better. This could also be
explained by the better overall performance of MPN methodology in
grossly contaminated waters (Santiago-Mercado and Hazen, 1987). It is
interesting that at Yauco, the membrane filtration and MPN methods
had similar performances.
Recovered bacterial densities in chlorinated drinking waters by
the methods tested reveal that MPN methods generally had better
performances (Tables 14 and 15). This fact had been noted previously
by Grabow et al (1981) in temperate waters. Notable exceptions were
m-KF recovered fecal streptococci at Bayam6n and Yauco. At these two
sites, membrane filtration was able to detect fecal streptococci when
MPN failed to do so. This could represent the survival of fecal
streptococci to standard treatment.
Influent nontarget bacteria recovered on fecal coliform media
were lowest when m-T7 agar was used (Table 15). The only site where
this was not true was at Guaynabo. The water source at Guaynabo is
Cidra Lake, which has been said to be grossly polluted due to illegal
domestic sewage discharges. Since the lake is very distant from the
treatment facility, water has to travel inside pipes for approximately 15
miles before reaching the plant. It is possible that the high pollution
level of the lake, combined with a possibly thick biofilm inside the
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42
pipes, provide optimum conditions for the growth of bacteria not
normally associated with water sources. These unusual bacteria might
be the-cause of the higher nontarget levels detected by m-T7 agar at
Guaynabo, This fact is supported by the observation that most of the
unidentifiable bacteria reported in Table 18 came from Guaynabo
influents.
Apart from the low levels of influent nontarget bacteria reported
on m-T7 agar, this medium also recorded the highest selectivity index
among the fecal coliform methods tested (Table 16). The specificity of
m-T7 agar was also the closest to the limits adopted by Pagel et al
(1982) for temperate waters. Perhaps the low performance of this
medium when assessing nontarget colonies (Tables 15 and 16) is due to
the ability of m-T7 agar to suppress some of the background growth
associated with tropicaJ source waters. m-T7 agar also recorded low
levels of confirmed nontarget colonies (Table 16). This might be an
indication that m-T7 agar, while still unacceptable for fecal pollution
assessment in tropical source water, might be useful for fecal coliform
isolation in this environment with a better chance of inequivocal
recovery in one step.
Studies have shown the influence of the type of water analyzed,
the media used, and techniques employed on the bacterial levels
detected at particular sites (Evans et al., 1981b). We found that
although the levels of total coliforms detected on the influents by
membrane filtration (MF) and MPN techniques were not different at
Villalba (Figure 5), they did differ at the other study sites (Figures 3-5).
A similar pattern was evident with methods for the detection of fecal
coliforms, Bayamon displayed no differences between methods for fecal
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43
coliform assessment (Figure 6), while at the other three sites, the
methods disagreed on the results (Figures 7-9). Fecal streptococci
assays revealed differences between the MF and MPN-methods only in
the Villalba effluent (Figure 10), the other three sites having similar
levels detected by each of the two methods used in both influents and
effluents. All influents had high total bacterial densities, as determined
by AODC (Figure 14).
In the sites exhibiting differences in the influent total coliform
levels detected by MF or MPN, MPN values were slightly lower than
those obtained by MF. This was caused by non-coliform interference on
the culture media used. The MPN technique has been criticized for its
failure to detect total coliforms under particular circumstances (Evans et
al., 1981a; Evans et al 1981b; Jacobs et al., 1986). Evans demonstrated
that if the total bacterial densities exceed 500 cells per ml, the MPN
technique results in decreased coliform densities being reported due to
competition of non-coliforms for nutrients, as well as by the production
by these of coliform inhibitory substances. The influents studied had
high total bacterial densities, plus high percentages of false positives
and negatives resulted from their assays, the lower densities of
coliforms in source waters as detected by MPN must be a result of
inhibition and masking by non-coliform organisms. The same
explanation applies to differences between the MF and MPN methods
used in the detection of fecal coliforms in tropical source water.
At Villaiba and Yauco, membrane filtration using m-T7 agar
resulted in significantly lower densities than MPN or MF with m-FC
agar. As already discussed, high numbers of false positives and
negatives occur when assaying for the presence of fecaJ coliforms in
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44
tropical source waters (Sannago-Mercado and Hazen, 1987). Examining
the confirmation rates we obtained for our fecal coliform assay
methods, we agree with these results. We also found that while the
fecal coliform densities detected by MF with m-T7 agar were lower than
those detected by m-FC agar, this medium has a higher reliability of
actually detecting the target fecal coliform rEscherichia coli) than other
methods. MF with m-T7 agar produced the highest confirmation
percentage of any of the three methods used. This is in agreement with
results obtained by LeChevallier at al (1983), who stated that m-T7
agar not only recovered fecal coliforms from chlorinated water, but
could also be used to detect coliforms in untreated surface waters with
a high reliability. MPN, on the other hand, produced the highest
percentage of unidentifiable microorganisms plus low confirmation
rates, confirming its unreliability due to common components of tropical
bacterial flora being able to grow in the media.
Examination of the species isolated from source waters by fecal
coliform assays (Table 18) reveals that m-T7 agar was able to detect E..
coli 4.2.7% of the time in the influents, as well as other organisms of
clinical importance, such as Shigella dysenteriae and Klebsiella-
p.ne.u maniac. All of this suggests that m-T7 agar may be more accurate
in the detection of the target fecal coliforms in tropical waters. This
medium performed equally well on chlorinated drinking water.
The occurrence of fecal coliforms in finished drinking water was
rare and most frequently related to increases in effluent turbidity.
However, there were a few cases when m-T7 agar was able to detect
fecal coliforms in the chlorinated effluents when m-FC agar and MPN
could not. In cases when E_. coli was detected in the effluents by MF
-------
using m-FC agar, m-T7 agar was able to detect 45% more £_. coli than rn-
FC and 55% more than MPN. This confirms the performance of m-T7
agar in tropical source waters in levels similar to those stated by
LeChevallier (1983) for temperate climates. However, since £,. coli
might be a normal inhabitant of tropical source water, the "accuracy" of
m-T7 agar may be useless in the tropics. Since pathogenic bacteria such
as Klebsiella pneumoniae. Shigella dysenteriae. and Pseudomonas sp.
isolated from m-T7 agar were isolated in higher proportions than in the
other fecal coliform methods (Tables 18 and 19), colonies from m-T7
could be screened for the presence of certain pathogens in tropical
source water. This approach towards the direct assessment of
pathogens could save time and effort, so its possibilities should be
further evaluated.
Results from the fecal streptococci assays on effluents also suggest
them as adequate indicators of fecal contamination of waters. We have
already discussed the occurrence of fecal streptococci in source waters
and the relationship between increases in their levels with rainfall. At
first, the occurrence of fecal streptococci in chlorinated drinking water
seemed to be strictly correlated to the occurrence of coliforms, but in
later samplings, fecal streptococci were detected in the effluents from
Guaynabo and Villalba even in the absence of coliforms. These
occurrences could not be correlated to any of the parameters under
study. Three possibilities exist: either the fecal streptococci detected
were able to withstand chlorination and recover from injury on the
complex media used, fecal coliforms were so injured that they failed to
recover even on m-T7 agar; or, as is the case with coliforms,
components of the normal microbiological flora at the study sites can
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46
produce false positives on these media. Since all media used to isolate
and confirm the presence of fecal streptococci contain sodium azide, any
organism capable of producing false positives on these media must not
rely on an electron transport chain with cytochrome oxidase for energy,
or they must be resistant to this chemical. The high selectivity indexes
of FS methods relative to other assays studied confirm this fact
However, studies in Puerto Rico have demonstrated the prolonged
survival rate of Streptococcus faecalis. the target organism, in a tropical
rain forest stream (Muniz et al., In Press). High densities of S_. faecalis
were also found to survive in tropical marine waters contaminated with
petroleum (Muniz et al., In Press). All of these findings discard the
possibility of fecal streptococci as adequate indicators of tropical
drinking water quality.
The issue of injured bacteria in tropical drinking water quality is
very important. Injured bacteria have been known to recover in
distribution systems due to favorable temperatures and increased
nutrient availability, posing health hazards to the community (McFeters
et al., 1986; Wright, 1932b). In the four filtration plants studied, the
total bacterial densities, determined by AODC were equal among
influents and effluents (Figures 12-15). This suggests that chlorination
might only be injuring coliforms, thus disabling them to grow on culture
media, but it is am effective in the disinfection of water. Several
mechanisms for the survival of bacteria in chlorinated water have been
proposed. Attachment or association of bacteria with various surfaces
can promote increased resistance to chlorine disinfection (Levy et al.,
1984; Tracy et al., 1966; Silverman et al., 1983; Camper et al., 1986;
Hejkal et al 1979; Hoff, 1978; LeChevallier et al., 1981). The production
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47
of extracellular capsules can also protect some bacteria against chlorine
(Reilly and Kippen, 1983; Clark, 1984). Members of the natural
microbiological flora of tropical waters may be capable of surviving the
standard disinfection procedures for the production of "potable water.
Effluent bacterial activity was lower than that of influents at all sites;
however, at Guaynabo, Villalba, and Yauco activities were not
significally different between influents and effluents (Figures 16-18),
providing further evidence of the little impact of the disinfection
procedures on the aquatic microbial communities at these sites. This is
startling, since high levels of total and free chlorine residuals (ranging
from 1.68 to 2.45 mg/L; See Table 1) were recorded in the effluents at
all sites. The concentration at Yauco was high enough to eliminate
Giardia sp. cysts (Rodriguez et ah, unpublished data). Since these cysts
are resistant to regular chlorination, the possibility of overchlorination
cannot be ruled out. Neither Giardia sp. nor Crvptospondium sp. cysts
were found at any of the other sites. However, problems along the
distribution line, such as broken pipes and collapses of bacterial biofilm
inside pipes may be hampering the performance of residual chlorine,
thus allowing high densities of bacteria to repopulate the finished
water. Overchlorination can result in yet another problem: the
formation of trihalomethanes. The occurrence of trihalomethanes is due
to the oxidation reactions in water treatment (chlorine disinfection)
with organic substances present in the water ("World Health
Organization, 1987). The most common trihalomethane, chloroform
(CHC13), produces cancer in laboratory animals (World Health
Organization, 1987). The carcinogenic potential of other common
trihalomethanes, such as CHBrCl;, CHBr:Cl, and CHBr} (formed in the
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43
casual presence of the bromide ion) is still being tested (World Health
Organization, 1987). The formation rate of trihalomethanes is
accelerated by high temperatures and pH as long as free residual
chlorine is still present in the water. Total trihalomethane
concentrations in drinking water can go up to 1000 ng/L (World Health
Organization, 1987).
Effluent activity at Bayamo'n was slightly lower than that of the
influents (Figure 19). This could be due to a slightly better performance
of the treatment process at this site. However, this plant also adds
fluoride to effluents as part of the treatment process, and the impact of
this chemical on the microorganisms studied in this research was not
measured. This is an area needing further investigation. We also note
that the percentage of bacteria active in protein synthesis in any sample
was highly variable at each site, probably due to the complex effects of
a high number of physico-chemical and bacterial parameters and the
interactions between them. Another possibility may be the seeding of
coliform bacteria in finished water by the sand filter beds used in the
treatment process. McFeters et al (1986) have shown that in temperate
waters, up to 6,300 coliforms can be associated with each grain of sand
in a filter bed. These coliforms can actually be inoculated into finished
drinking water, as water pressure dislodges clumps from the filter bed
that can survive chlorinadon due to their close association with each
other (McFeters et al.,1986). The higher densities of both coliforms and
false positive coliforms in tropical waters might accumulate in much
higher numbers in the filter beds, especially if these are not
backwashed every day. Thus, in the tropics, coliforms seeded into
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49
drinking water may be a common occurrence. This adds further
uncertainty to the problem of drinking water quality in the tropics.
These findings point towards the need for closer and careful
monitoring of the methodology currently being used to" determine
drinking water quality in the tropics. We also see the need for the
identification and proper management of water resources of relatively
variable and reliable high quality to be used as potable water sources;
this conclusion has been supported by other investigations of both
tropical and temperate drinking water quality (National Academy of
Sciences, 1977). Further research on the effects of physical and bacterial
parameters and their interactions on water quality would also be
needed at each of these water sources to develop and manage them in
the most effective way. A better indicator of water quality is needed to
solve the problems of water pollution control and drinking water
quality in the tropics. Direct enumeration of pathogens, while costly,
could be a solution. However, other indicators have been proposed
(Dutka, 1978).
Bifidobacterium sp. have been proposed as tropical water quality
indicators (Evison and James, 1975). Since B ifidobacterium sp.-are
obligate anaerobes, they are not able to survive in most extraenteral
environments. In addition, Bifldobacteria are commonly found in the
gut of humans, but not in animals (Hazen, 1988). Carrillo et al (1985)
showed that the densities of Bifidobacterium adolescentis. decreased one
order of magnitude per day in a tropical rain forest stream in Puerto
Rico. These investigators also found that the densities of bifidobacteria-
ILke organisms were greater than those of £.. colj at all sites, except those
at a sewage outfall. The media developed for the isolation of
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50
bifidobacteria (YN-6) had low specificity if background anaerobic
bacterial densities were high (Carrillo et al., 1985). In fact, less than
80% of isolates from a Puerto Rican rain forest stream were actually
bifidobacteria. Bifidobacteria shows promise as an indicator of human
fecal contamination in terms of their specificity and lack of survival in
situ. The development of media with better specificity in tropical
waters would make bifidobacteria excellent indicators.
While Clostridium perfringens. another anaerobe, has been
proposed as yet another indicator alternative (Fujioka and. Shizumura,
1985; Bisson and Cabelli, 1980), its densities in human feces from the
tropics are much lower than those of the currently used indicators
(Wright, 1982a). In addition, clostridial spores can resist disinfection if
the chlorine concentrations and pH are inadequate. These facts propose
serious doubts as to the ability to detect £.. perfringens easily when an
eminent danger of pathogens exists (Wright, 1982a; Hazen, 1988).
Wright(1982a) could not correlate the isolation of £.. perfringens with
the occurrence of Salmonella sp. in tropical source water. Furthermore,
Carrillo et al (1985) found that the densities of total anaerobes were
high in sites that had received torrential rainfall. This, along with the
increased densities of £.- perfringens reported in uncontaminated sites
after rainfall (Fujioka and Shizumura, 1985), suggests that £..
pej fringes might have an extraemeral source in the tropics (Hazen,
1988). While bifidobacteria and Clostridium perfringens might be more
suitable indicators for the tropics, we must also take into account that
techniques involving the assessment of these anaerobes require more
sophisticated and expensive techniques (Franzblau et al., 1984).
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5 1
Recent studies in Puerto Rico have posed coliphages as more
specific than £.. coli in terms of pollution detection. Toranzos and Gerba
(1988) demonstrated that coliphages were only detected in water
receiving sewage effluents. The viradel technique (Goyai and Gerba,
1983) was used to concentrate the viruses in one liter water samples.
Escherichia coli C3000 (ATCC 15597) was then used for coliphage
assessment. Since the assay is specific for a particular strain of £,. coli.
the problematic tropical background flora are eliminated. The only
problem is that if E.. colj is able to survive for prolonged periods in
tropical waters, the viruses that infect them could do so too. Coliphage
assays would only be appropiate in tropical environments if it was
demonstrated that only one strain of £.. coli is not capable of
extraenteral survival (Hazen, 1988). Studies to date (Elias et al., 1988;
Lopez et al., 1987; V aide's-Collazo et al., 1987; Hazen et al., 1981;
Jimenez et al., 1988; Perez-Rosas et al., 1984; Carrillo et al., 1985) have
shown this to be unlikely, since all tested strains of E. coli were capable
of extraenteral survival in tropical waters.
We have already mentioned the phenomena of viable but
unculturable bacteria. This has been show to happen with many
pathogens in both temperate and tropical waters, which suggests that
most of the time, indicators may not be correlated to disease risk
(Baker et al., 1983; Colwell et al., 1985; Hazen et al., 1987). Considering
this, the best way to assess tropical source water might well be the
direct enumeration of the pathogens themselves. Several approaches to
pathogen enumeration have been proposed. Among these,
immunofluorescent staining have been shown to detect extremely low
HO cells/ml) levels of pathogenic bacteria (Colwell et al., 1935). Cross
-------
52
reactivity, however, hampers the potential of this approach, even -A hen
monoclonal antibodies are used.
Currently, DNA, cDNA, or RNA probes may be the most sensitive
method for pathogen detection. DNA probes have been developed for
Salmonella sp., and for enterotoxigenic £.. coli (Hazen, 1938). The
detection of any of these in tropical drinking water would surely point
to an imminent human health hazard.
-------
53
CONCLUSIONS
1. Turbidity is a very important factor affecting water treatment. By
associating themselves with paniculate matter, bacteria gain
protection against chlorine disinfection. Whenever water turbidity
increases, special care should be taken in analyzing samples to
determine bacterial water quality.
2. Turbidity also affects the performance of the media used for the
detection of indicator bacteria. The high levels of background
bacteria brought about by high turbidity of both influents and
effluents can interfere with the expression of the characteristics
normally associated with indicator bacteria on differential media.
High turbidity also clogs membrane filters and makes it difficult to
observe bacterial growth on fermentation tubes.
3. Coliforms may survive treatment and remain in an unculturable,
but viable (injured) state. Injured coliforms cannot be detected
using standard methodology. This can result in water being
classified as potable when it is not. The only method capable of
detecting injured coliforms was membrane filtration using m-T7
agar.
4. Membrane filtration using m-T7 agar recovered less nontarget
bacteria than any other fecal coliform method. This method could
be used to recover naturally occurring coliforms with higher
selectivity in tropical environments.
5. Fecal streptococci are inadequate indicators of water quality in the
tropics. Some components of the normal environmental flora are
able to give false positive fecal streptococci reactions on standard
-------
54
media. In addition, further investigation is needed to determine
the higher resistance of fecal streptococci to chlorine disinfection
and the occurrence of tropical fresh water bacteria capable of
growing on culture media containing sodium azide7
6. Although standard water treatment was able to significantly reduce
the total bacterial densities present in source water, AODC was able
to detect high concentrations of bacteria in drinking water. Further
research as to the nature and identity of this background flora is
needed.
1. Due to the high numbers of fecal coliform positive bacteria of non-
fecal origin, water treatment plants in Puerto Rico are
overchlorinating finished drinking water. Thus, not only is the
chlorination process treating a health risk, it is creating one by
increasing the concentration of trihalomethanes in the water.
8. In terms of bacterial densities, raw water brought in from Cidra
Lake to the Guaynabo treatment plant had unique characteristics
relative to those of the other plants studied. The possible effects of
water travel through such a long pipeline, in terms of bacterial
quantity and quality, should be further studied.
9. New, better indicators of tropical drinking water quality are an
immediate necessity. The only practical solution to this problem
might be the direct isolation of pathogenic microorganisms from
drinking water.
-------
55
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o 8
Table I. Water Quality Parameter by Site and Water Source. (Measurements were taken between September M,
1987 and March 7, 1988).
INFLUENTS
Site
Bayam6n
Guaynabo
Villalba
Yauco
EFFLUBTO
Site
Bayamon
Guaynabo
Villalba
Yauco
Temperature
CO
1
24.45 ± 0.39
24.00 ± 0.63
22.50 ± 0.23
21.46 ± 0.37
Temperature
TO
25.18 ± 0.30
25.00 ± 0.39
22.67 ± 0.22 '
23.15 ± 0.32
PH
7.76 ± 0.13
7.36 ±0.17
7.86 ± 0.18
7.96 ± 0.10
PH
7.79 ± 0.17
7.20 ±0.16
7.57 ±0.19
7.73 ± 0.19
TCI'
(mg/L)
0.02 ± 0.02
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
TCP
(mg/L)
1.13 ± 0.08
1.34 ± 0.17
2.41) ±0.15
1.43 ± 0.09
FCI2
(mg/L)
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
0.00 ± 0.00
FC12
(mg/L)
0.96 ± 0.06
0.68 ± 0.10
1 68 ± 019
0.89 ± 0.08
Turbidity
(NTU)
34.03 ± 9.61
19.48 ± 8.77
60.75 ± 30.00
6.57 ± 1.89
Turbidity
(NTU)
1.60 ± 0.28
1.19 ± 0.12
1 99 ± 0 20
1.12 ± 0.14
All values are mean ± one standard error, 1 = Total chlorine, 2 = Free Chlorine.
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I able 2. Bacteriological Density by Site and Water Source. (Measurements were taken between September 24.
and March 7, 1988).
INPUJENTS
m-Endo
Site
Bay a rn 6 n
Guaynabo
Villalba
Yauco
(CPU)
1074 ±
3662 ±
28475
1808 ±
553
1835
± 2030
859
m-FC
(CPU)
190 ±
84
1510 ± 667
903 ±
233 ±
354
89
m-T7
(CPU)
80 ± 46
439 ± 265
146 ± 45
127 ± 77
m-KF
(CPU)
203 ± 68
1928 ± 1006
950 ± 471
948 ± 333
MPN1
133 ± 79
1285 ±
1068 ±
949 ±
166
172
204
MPN2
62 ± 30
969 ± 1
984 ± 1
564 ± 1
98
77
76
MPN 3
94 i 4 A
1 1 23 1 !
1 1 2(> ± 1
930 + 20
EFFLUENTS
m-Endo
Site
Bayam6n
Guaynabo
Villalba
Yauco
(CPU)
0.00 ±
0.25 ±
0.75 ±
0.62 ±
0.00
0.18
0.79
0.37
m-FC
(CPU)
0.00 ±
0.00 ±
2.00 ±
0.54 ±
0.00
0.00
2.00
0.37
m-T7
(CPU)
1.18 ± 1.18
O.(X) ± 0.00
0.45 ± 0.45
0.09 ± 0.09
m-KF
(CPU)
6.36 ± 5.40
0.17 ± 0. 1 1
1.92 ± 0.79
4.31 ± 3.46
MPN1
2.00 ±
2.92 ±
2.00 ±
2.46 ±
0.00
0.92
0.00
0.46
MPN 2
2.00 ±
3.75 ±
2.50 ±
2.00 ±
0.00
1.75
0.50
0.00
MPN 3
2.91 d
12.7 d
2.00 J
2.46 d
All values arc mean ± one standard error per 100 ml. MPN1 = Total coliforrn MPN. MPN2 = Pecal coliform MPN.
= Fecal streptococci MPN
-------
Table 4. Average Indicator Organism / AODC* Ratios per Viable Count Method Assessed
(Expressed as Percentages).
INFLUENTS
7 I
SITE
Bayam6n
Guaynabo
Villalba
Yauco
m-Endo
agar
i
7.29
313
96
98
m-FC
agar
2.63
128
15.7
12.9
m-T7
agar
1.21
33.7
2.39
2.50
m-KF
agar
3.64
108
20.2
66.4
MPN1
2.25
191
19.8
43.0
MPN2
9.24
166
19.6
26.0
MPN3
1.56
189
21.0
68.0
EFFLUENTS
SITE
Bayamon
Guaynabo
Villalba
Yauco
m-Endo
agar
0.00
0.00
0.02
0.05
m-FC
agar
0.00
0.00
0.07
0.03
m-T7
agar
0.07
0.00
0.02
0.00
m-KF
agar
0.33
0.04
0.06
0.38
MPN1
0.14
3.27
0.15
0.28
MPN2
0.14
4.60
0.24
0.27
MPN3
0.19
19.0
0.15
0.28
All valures are percent X 10-4, MPNl=Total coliform MPN, MPN2=Fecal coliform MPN, MPN3=
Fecal streptococci MPN.
-------
7 0
Table 3. Direct Count and Percent Activity by Site and Sample Type (Measurements taken between Scptemlx-r M.
1987 and March 7, 1988)
BAYAMON GUAYNABO
S a m p 1 c
Type
Influent
Effluent
Sample
Type
Influent
Effluent
AGDC*
(X 107)
7.19 ± 3.67
1.74 ± 0.44
VILLALBA
AODC*
(X 107)
3.44 ± 0.46
1.42 ± 0.27
Percent
Activity**
73.84 ± 4.15
56.31 ± 3.58
Percent
Activity**
76.38 ± 3.65
62.69 ± 6.27
Sample
Type
Influent
Effluent
AODC*
(X 107)
4.39 ± 0.76
0.82 ± 0.41
Percent
Activity1"*'
32.14 ± <>.?.
26.53 i 5.6
YAUQO
Sample
Type
Influent
Effluent
AODC*
(X 107)
5.02 ± 0.96
2.53 ± 0.38
Percent
Activity**
50.45 ±
36.5K ±
*Acridir»e Orange Direct Counts.
**as determined by the number of red fluorescing cells divided by the total number of fluorescent cells and
multiplied by 100.
-------
Table 5. Correlation Matrix: Combined Influent Bacteriological Methods and Water Quality
MFTC
MI: TC
MF FC
MF MT7
MF FS
MPNTC
MPN FC
MPN FS
AdX
%ACT.
TEMP
PH
TURB
1
0
0
0
0
0
0
0
0
0
0
0
.000
.889***
i
.708**
.818**
.828***
.798**
.766**
.083
.052
.218
.117
.064
MFPC
1
0
0
0
0
0
0
0
0
0
0
.000
.699**
.724**
.738**
.732**
.687**
.004
.101
.185
.175
.083
MFMT7
!
0
0
0
0
0
0
0
0
0
.000
.552
.594*
.619*
.648*
.042
.128
.223
.049
.098
MFFS
1.000
0.698**
0.706**
0.68*
0.097
0.100
0.027
0.241
0.140
MPNTC
1.000
0.884***
0.888***
0.167
0.216
0.233
0.135
0.056
MPN FC
1.
0.
0.
0.
0.
0.
0.
000
902***
127
117
314
147
020
MPN FS ACOC %ACT TEMP PH TUKH
1.000
0.232 1.000
-0.146 0.345 1.000
0.253 0.124 0.028 1.000
-0.108 0.206 0.039 0.440 1 .000
0.01 1 0.380 0.435 0.090 0.033 1.000
n=44, * = P < 0.05, ** - P < 0.01. *** » P < 0.001. MF TC - m-Endo agar, MF FC - m FC agar. MF MT7 - m-T7 «g«r. MF FS - ra-KF a^ar. MPN
Total coliform MPN. MPN FC « Fecal coliform MPN. MPN FS = Fecal streptococci MPN. AODC » Acridine Orange Direct Counts. %ACT =
Percent activity, TEMP » Temperature. TURB = Turbidity.
-------
Table 6. Correlation Matrix: Combined Effluent Bacteriological Methods and Water Quality.
MFTC
MP FC
MF MT7
MFFS
MPN TC
MPN FC
MPN FS
AODC
%ACT
TEMP
PH
TURB
TCL
FOL
MFTC
1.000
0.839***
0.022 .
0.306
0.23!
-0.077
0.089
0.225
-0.151
-0.032
0.153
0.229
0.220
0.188
MFFC
1.000
0.035
0.221
0.215
-0.051
0.095
0.117
-0.161
-0.066
0.106
0.221
0.098
0.121
MF MT7
1. 000
0.448
0.080
-0.050
0.382
0.097
0.031
0.038
0.106
0.540
0.041
0.021
MFFS
8.000
0.084
-0.052
0.247
0.215
0.163
-0.107
0.353
0.478
0.152
0.184
MPNTC
1.000
0.573
0.856*
-0.109
-0.334
0.054
0.067
0.087
-0.136
-0.101
MPNFC
1.000
0.649
-0.279
-0.192
0.036
-0.008
0.247
-0.690
-0.036
MPN FS
1.000
-0.162
-0.247
0.115
0.113
0.316
-0.1 13
-0.093
AOUC
1.000
0.341
-0.058
0.069
0.148
0.283
0.268
%ACT TEMP PH TURB TU . I-C
1.000
-0.102 1.000
0.192 -0.262 1.000
0.277 -0.112 0.195 1.000
0.446 -0.484 0.024 0.334 1 .()()()
0.422 -0.567 0.025 0.321 0.9W*-
n=46, * - P < 0.05, ** - P < 0.01. *** = P < 0.001. T CL = Tola! Chlorine. F CL - Free Chlorine..MF TC - m-Endo agar. MF FC - m-FC aj;»r.
MT7 = m-T7 agar. MF FS - m-KF agar. MPN TC = Total coliform MPN. MPN FC - Fecal coliform MPN. MPN FS « Fecal streptococci MPN
= Acridinc Orange Direct Counts. %ACT = Percent activity, TEMP = Temperature. TURB ^ Turbidity.
-------
Table 7. Correlation Matrix: Bayam6n Influent Bacteriological Methods and Water Quality.
7 4
MFTC
MF F:C
MF MT7
MF f S
MPN TC
MPN I-C
MPN PS
AOCX
%ACT
TEMP
PH
TURB
MFTC
1.000
0.933***
0.742** '
0.625*
0.732**
0.629*
0.616*
0.665*
0.509
0.875***
0.612*
0.028
MFFC
1.000
0.804**
0.53
0.805**
0.652*
0.7*
0.477
0,31
0.881***
0.714**
0.027
MFMT7
1 .000
0.517
0.739**
0.503
0.675*
0.499
0.222
0.718**
0.471
0.089
MFFS
1.000
0.372
0.569
0.316
0.472
0.554
-0.566
0.231
0.247
MPNTC
1 .000
0.771**
0.953***
0.335
0.152
0.624*
0.547
0.131
MPNFC
1.000
0.823***
0.525
0.478
0.66*
0.555
0.264
MPN FS
1.000
0.373
0.134
0.601*
0.628*
0.319
AOUC %ACT TEMP PH TURH
1.000
0.623* 1.000
0.559* -0.548 1.000
0.354 0.24 0.839*** 1.000
0.073 -0.087 -0.036 0.356 1.000
n=l 1, * = P < 0.03, ** - P < 0,01, *** = P < 0.001. MF TC - m-Endo agar, MF FC - m-FC agar, MF MT7 - m-T7 ngnr, MF FS - m-KF agar. MPN
Total coli/orm MPN. MPN FC » Fecal conform MPN. MPN FS = Fecal streptococci MPN. AODC « Acridirws Orange Direct Counts, %ACT =
Percent activity, TEMP - Temperature. TURB = Turbidity.
-------
Table 8. Correlation Matrix: Bayam^n Effluent Bacteriological Methods and Water Quality.
MFTC
MFFC
MF MT7
MI- FS
MPNTC
MPN FC
MPN FS
AGDC
%ACT
TEMP
PH
TURB
TCL
FCL
MFTC MFFC MF MT7
1.000
1 .000
i
1 .000
0.845***
« a «
» • «
» * *
0.109
0.108
0,056
0.299
0.952***
0.600
0.529
MF FS MPN TC
1. 000
1 .000
» •
0.845*** •
0.095
0.108
0.221
0.234
0.782** •
0.630*
0.662*
MPN FC MPN FS
1.000
1 .000
-0.109
0.108
-0.056
0.299
0.952***
0.600**
0.529
AODC %ACT TEMP PH TURB 1X1, K
1.000
0.431 1.000
0.187 0.252 1.000
0.520 0.080 0.184 1.000
0.054 0.299 0.138 0.262 1.000
0.231 0.316 0.090 0.008 0.733** 1.000
0.415 0.303 0.100 0.137 0.640** 0.927**
n=l I, * = P < 0.05. ** « P < 0.01, *** * P < 0.001, • - Zero values correlation. T CL « Total Chlorine. F CL » Free Chlorine..MF TC = m-Kndo
agar. MF FC » m-FC agar. MF MT7 - nvT7 agar, MF FS - m-KF agar, MPN TC - Total coliform MPN. MPN FC - Fecal conform MPN. MPN F:
Fecal streptococci MPN, AODC * Acridine Orange Direct Counts. %ACT = Percent activity. TEMP * Temperature, TURB = Turbidity.
ACT = Percent activity; TEMP = Temperature; PH = pH; TURB = Turbidity.
-------
Table 9. Correlation Matrix: Guaynabo Influent Bacteriological Methods and Water Quality
MFTC
Mr i:c
MF MT7
MF FS
MPN TC
MPN PC
MPN FS
AODC
%ACT
TEMP
PH
TURB
MFTC
1.000
0.934***
0.387 i
0 931***
0.634*
0.516
0.457
0.244
0.473
0.458
0.204
0.79*
MFFC
J.OOO
0.597*
0.796**
0.576*
0.659*
0.508
0.130
0.471
0.605*
0.081
0.656*
MFMT7
1.000
0.376
0.204
0.817**
0.634*
0.553
0.337
0.671*
0.231
0.015
MFFS
1.000
0.286
0.291
0.275
0.459
0.259
0.166
0.080
0.78*
MPNTC
1.000
0.341
0.602*
0.172
0.291
0.686*
0.213
0.254
MPNFC
1.000
0.740**
0.460
0.641*
0.737**
0.239
0.260
MPNFS ACDC %ACT TEMP PH TURK
1.000
0.562 1 .000
0.649* 0.184 1.000
0.687* 0.462 0.169 1.000
0.050 0.318 0.621** 0.290 1.000
0.220 0.618* 0.474 0.030 0.591* 1000
n=l I. * = P < 0.05, ** « P < 0.01, *** = P < 0.001, MF TC = m-Endo agar. MF FC » m FC ngnr. MF MT7 - m-T7 *g«r. MF FS - m-KF a^ar. MF
Total coliform MPN. MPN FC =* Fccnl colifomi MPN, MPN FS » Fcca! streptococci MPN, AODC » Acridinc Orange Direct Counts, %ACT
Percent activity, TEMP = Tcmperauire, TURB = Turbidity.
-------
Table 10. Correlation Matrix: Guayraabo Effluent Bacteriological Methods and Water Quality.
77
MFTC
MF FC
MFMT7
MF FS
MPNTC
MPN FC
MPN FS
AOOC
%ACT
TEMP
PH
TURB
TCL
FCL
MFTC MFPC
LOGO
, ! .000
« •
0.194
0.131
0.131
0.131
0.617*
0.258
0.336
0.000
0.202
0.397
0.383
MF MT7 -MF FS
1. 000
1 .000
-0.135
-0.135
-0.135
0.179
0.253
0.173
0.068
-0.316
0.087
0.157
MPNTC
1.000
•
-
0.273
0.249
0.000
0.233
0.371
0.076
0.010
MPNFC
1 .000
•
0.273
0.249
0.000
0.233
0.371
0.076
0.010
MPN FS
1 .000
0.000
0.249
0.000
0.233
0.371
0.076
0.010
ACDC %ACT TEMP PH TURB TU. K
1.000
0.182 1.000
0.438 0.340 1.000
0.342 0.480 -0.370 1.000
0.123 0.407 -0.062 0.528 1.000
0.399 0.481 0.289 0.132 0.481 1.000
0.403 0.406 0.273 0.118 0.523 0.979**
ri=ll. * = P < 0.05, ** - P < 0.01. *** «= P < 0.001, • = Zero values correlation, T CL = Total Chlorine. F CL *» Free Chlorine..MF TC = m-F.ruln
agar, MF FC « m-FC iigar. MF MT7 = rn-T7 agar. MF FS = m-KF ngar. MPN TC = Total colifonm MPN, MPN FC » Fecal colifonm MPN. MPN 1
Fecal streptococci MPN. AODC * Acridine, Orange Direct Counts. %ACT = Percent ncttvity. TEMP = Terhperaiurc, TURB = Turbidity
ACT = Percent activity; TEMP = Temperaiure; PH = pH; TURB = Turbidity.
-------
Table 11. Correlation Matrix: ViSSalba Influent Bacteriological Methods and Water Quality.
7 S
MFTC
MF FC
MF MT7
MF F~S
MPN TC
MPN FC
MJ'N FS
AGDC
%ACT
TEMP
PH
TURB
MPTC
1.000
0.654*
0.739**
0.643*
0.496
0.761**
0.61*
0.138
0.051
0.587*
0.685*
O.H6
MFPC
1.000
0.497
0.331
0.268
0.689*
0.639*
0.080
0.059
0.340
0.554
0.120
MFMT7
i .000
0.4R6
0.296
0.596*
0.76**
0.193
0.364
0.817**
0.525
0.384
MFFS
1 .000
0.547
0.482
0.482
0.330
0.008
0.278
0.271
0.374
MPNTC
1 .000
0.443
0.127
-0.287
-0.268
0.178
-0.454
-0.057
MPNFC
1.000
0.786**
0.419
0.271
0.504
0.410
0.176
MPN FS ACIDC %ACF TEMP PH THRU
1.000
0.340 1.000
0.088 0.577* 1.000
0.653* 0.449 0.069 1.000
0.452 0.119 0.069 0.532 1.000
0.432 0.239 0.490 0.144 0.073 1.000
n=i j. * = P < 0.05, ** - P < 0.01. *** » P < 0.001. MF TC * m-Emlo agar. MF FC » m-FC BRftr. MF MT7 » m-T7 ngar. MJF FS - m-KF agar. MF
Total colifomi MPN, MPN FC «• Fecal coliforns MPN. MPN FS =• Fcc«> sircptococci MPN. AODC » Acridine Orange Direct Counts. %AC~I
F'crccni activity. TEMP » Temperature. TURB = Turbidity.
-------
Table 12. Correlation Matrix: Vilialba Effluent Bacteriological Methods and Water Quality.
7 9
MFTC
MF FC
MF MT7
MF FS
MPN TC
MPN FC
MPN FS
AODC
%ACT
TEMP
PH
TURB
TOL
FOL
MFTC
1.000
0.873***
0.128
0.026
«
0.128
•
0.131
0.398
0.363
0.248
0.277
0.335
0.333
MFFC
1 .000
0.091
0.237
a
0.091
•
0.075
0.430
0.539
0.014
0.228
0.030
0.042
MFMT7
1 .000
0.237
•
0.091
•
0.054
0.281
0.135
0.150
0.041
0.269
0.333
MFFS
s.ooo
e
0.004
•
0.255
0.358
-0.419
0.414
0.081
0.447
0.470
MPNTC MPNFC MPN FS
1.000
1 .000
1 .000
0.430
0.258
0.135
0.033
0.588*
0.508
0.457
AODC %ACT TEMP PH TURB K3, K
r
1 .000
0.145 1.000
0.458 0.100 1.000
0.264 -0.237 -0.609* 1.000
0.134 -0.246 0.327 0.07! 1.000
0.244 0.318 -0.355 0.255 -0.403 1.000
0.234 0,290 -0.316 0.194 -0.371 0.981***
n=ll. * = P < 0.05, ** * P < 0.01, *** * P < 0.001, • » Zero values correlation. T CL =* Tolal Chlorine, P CL » Free Chlorine,.MF TC = rn-Hmlo
agar, MF FC = m-FC «gar. MF MT7 == m-T7 agar, MF FS * m-KF agar. MPN TC - Total coliform MPN. MPN FC « Fccn! coliform MPN, MPN FS
Fecal slrcplococci MPN, AODC = Acridine Orange Direct Counts, %ACT = Percent acliviiy. TEMP » Tcmpernture, TURB = Turbidiiy.
ACT = Percent activity; TEMP = TefTipcif'ature; PH = pH; TURB = Turbidiiy.
-------
Table 13. Correlation Matrix: Yaucp Influent Bacteriological Method"? and Water Quality.
X O
MFTC
MFTC
MF FC
MF MT7
MF FS
MPN TC
MPN FC
MPN f^S
AODC
%ACF
TEMP
PM
TUKB
1
0
0
0
0
0
0
0
0
0
0
0
.000
.923***
.658* '
.972***
.915***
.885***
.782**
.411
.029
.696*
.499
.045
MFPC
1.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
000
616*
893***
818***
8 I 3 * * *
694*
440
079
586*
628*
204
MFMT7
1.000
0.615*
0.678*
0.761**
0.724**
0.197
0.401
0.239
0.278
0.242
MFFS
1.000
0.833***
0.807**
0.723*
-0.492
-0.021
0.645
-0.430
0.023
MPNTC
1.
0.
0.
0.
0.
0.
0.
0.
000
98***
851***
241
055
722**
669*
004
MPNFC
1.000
0.835"*
0.136
0.191
0.678*
0.682*
0.099
MPN FS
1.000
0.195
0.021
0.728**
0.581*
0.135
AODC %ACT TEMP PH TURN
1.000
0.732** 1.000
0.404 0.026 1.000
0.18 5 0.123 0.562 1.000
0.558 0.571 0.261 0.227 1.000
n=M, * = P < 0.05. ** - P < 0.01, *** » P < 0.001. MF TC = m-Endo agar, MF FC = m FC agar. MF MT7 » m-T7 agnr. MF FS - m-KF agar. MPN
Total coliform MPN, MPN FC * Fecal col i form MPN. MPN FS = Fecal streptococci MPN. AODC » Acridinc Orange Direct Counts, %ACT =
Percent activity, TEMP » Temperature, TURB = Turbidity.
-------
Table 14. Correlation Matrix: Yauco Effluent Bacteriological Methods and Water Quality.
MFTC
MF FC
MF MT7
MFFS
MPN TC
MPNFC
MPN FS
AODC
%ACT
TEMP
PH
TURB
TO.
FCL
MFTC
1.000
» i
0.731***
0.903***
0.619*
«
0.681*
0.345
0.255
0.4J6
0.236
0.066
0.342
0.331
MFPC
1.000
0.731**
0.903***
0.619*
•
0.611*
0.345
0.255
0.416
0.236
0.066
0.342
0.331
MFMT7
! .000
0.426
0.893***
•
0.884***
0.420
0.317
0.280
0.015
0.246
0.073
0.010
MFFS
1.000
0.323
«
0.317*
0.158
0.154
0.331
0.374
0.066
0.402
0.403
MPN TC
1.000
•
*
0.584*
0.481
0.263
0.043
0.114
0.204
0.119
MPN FC MPN FS
1.000
1.000
0.589*
0.486
0.261
0.044
0.108
0.209
0.123
AOUC %ACT TEMP PH TURB '1X1, K
1 .000
0.687** 1 .000
0.169 0.010 1.000
0.289 0.206 0.207 1.000
-0.039 0.290 0.273 0.57* 1 .000
-0.326 0.149 0.436 0.187 0.022 1.000
-0.2.40 0.029 0.440 0.216 0.039 0.984***
n=| I. * = P < 0.05. ** » P < 0.0f, *** => P < 0.001, • = Zero values correlation, T CL = Total Chlorine. F CL « Free Chlorinc,.MF TC - m-Hndo
agar. MF FC = ro-FC agar. MF MT7 = m-T7 flgar, MF FS = m-KF agar. MPN TC = Toial coliform MPN, MPN FC = Fecal coliform MPN. MPN FS
Fecal sircptococcs MPN. AODC = Acridine Orange Direct Counts. %ACT = Percent acliviiy. TEMP •> Temperature. TURB » Turbidity
ACT = Percent activity; TEMP * Temperblurtc; PH =« pH; TURB * Turbidily.
-------
Table 15. Media Performance in the Assessment of Target Bacteria.
INI-UJKNTS
Ayciflfte Recovery*
Endo FC T7
Bayam6n 422
Guaynabo 1912
Villalba 4335
Yauco 1276
144
1087
539
217
66
1 39
99
101
KF
143
558
401
1618
MPN1
133
1256
1019
963
MPN2
61
91 1
928
479
MPN3
94
1079
1086
975
Hi ft K ^* - ---- — "> I o w
Endo>FC=T7=KF=MPN 1 =MPN2=M PN 3
Endo=FC=T7=KF=MPN 1 = MPN2=M PN 3
Endo>MPN 1 =MPN2=MPN3>FOT7 =• K I •
Endo=FC=T7=KF=MPNl=MPN2=MJ'N3
0-TLUENTS
AiLCJLag;
Endo
Bayam6n 0
Guaynabo 0
Villalba 0
Yauco 0
c Rccov*
FC
0
0
2
0
?ry*
T7
1
0
0
0
KF
6
0
2
5
MPN1
0
1
0
2
MPN2
1
2
2
0
MPN3
0
1 2
0
1
rcrfoxma.fl.cct
Endo=FC=T7=KF=MPNl=MPN2=MPN3
Endo=FC=T7=KF^=MPN 1 =MPN2=MPN3
Endo=FC=T7=KF^MPN 1 =MPN2=M PN 3
Endo=FC=T7-KF=MPNl=MPN2=MPN3
*Average Recovery = Average of the target colonies recovered on each plate, Endo=m-Endo agar, FC=m=FC agar,
17=rn-T7 agar, KF=m-KF agar, MPNI=Total coliform MPN. MPN2=Fecal coliform MPN. MPN3=Fccal streptococci M.
tPerformance as measured by Student Newman-Keuls (SNK) Test.
-------
Table 16. Media Performance in the Assessment of Nontarget Bacteria.
1NHJJENTS
Average JRccoyery"'
Endo FC T7
Bayarn6n
Guaynabo
Villalba
Yauco
702
1923
2208
821
54
349
273
28
14
109
46
25
KF
63
87
174
287
MPN1
72
606
182
370
MPN2
20
260
316
67
MPN 3
45
136
228
236
tJCJUli?JLJBAn.Cl£!.T
lliph^C - ~ -^1 ow
Endo>FC=T7=KF=MPNl=MPN2=MPN3
Endo=FC=T7=KF=MPN 1 =MPN2=MPN3
Endo=FC=T7=KF=MPN 1=MPN2=M PN 3
Endo=KF=MPNl>FC=T7=MPN2=MPN3
EIT-LUENTS
Average Recovery*
Endo PC T7
Bayamon
Guaynabo
Villalba
Yauco
1
0
0
0
6
0
0
2
4
1
0
0
0
1 1 ; f ' i
KF
4
0
1
2
MPN1
2
3
1
1
MPN2
2
4
2
1
MPN3
2
1 4
3
2
Endo=FC=T7=KF=MPNl=MPN2=MPN3
Endo=FC=T7=KF=MPNl=MPN2=MPN3
Endo=FC=T7=KF=MPN 1 =MPN2=MPN3
Endo=FC=T7=KF=MPNl=MPN2=MPN'3
*Average Recovery = Average of the target colonies recovered on each plate, Endo=rn-Endo agar, FG=m=FC agar
17=m-T7 agar. KF=m-KF agar. MPNl=Total coliform MPN, MPN2=Fecal coliform MPN, MPN3=Fecal streptococci
fPerformance as measured by Student Newman-Keuls (SNK) Test.
-------
K -1
Table 17. Specificity and Selectivity of Viable Count Methods in the Assessment of Influents.
PARAMETER
Presumptive Target
Verified Target
Presumptive nontarget
Verified nontarget
Specificity Indexes (%):
False Positive Errors
False Negative Errors
Selectivity Index (%)
n. d
ag
1 7
10
24
1 4
4 1
50
4 1
o m-FC
agar
37
26
22
1 6
30
1 9
63
m-T7 F
agar
20
15
1 0 4
8
25
1 2 3
<•, f. 3
MPN1
26
12
15
9
54
33
63
MPN2
30
1 9
28
20
37
29
52
MPN3
1 5
1 1
39
28
27
45
29
MPNl=Total coliform MPN. Mr
2-.fViv..\J
I'N.'i
ptococx
-------
Table 18. Specificity and Selectivity of Viable Count Methods in the Assesment of Effluents.
METHODS
PARAMETER
Presumptive target
Verfified target
Presumptive nontarget
Verified nontarget
Specificity Indexes (%)
False positive error
False negative error
Selectivity index (%)
m-Endo
agar
20
7
17
5
65
63
54
m-FC
agar
25
1 3
1 9
8
24
46
57
m-T7
agar
19
12
1 6
10
37
33
54
m-KF
agar
1 5
10
7
4
33
23
68
MPN1
21
6
7
3
7 1
40
75
MPN2
31
1 4
35
1 5
55
59
47
MPN3
1 5
9
4
2
40
1 8
79
MPNl=Total coliform MPN. MPN2=Feca! coliform MPN. MPN3=Fecal streptococci MPN.
-------
Table 19. Bacterial Species Isolated from Influent Waters.
Number of
Species
J^tlicjdthia £fiil
Acromonas hytfrophv
Unidentified*
Vibrio uiiirJLLCJLS
Pscudomopas sp.
Klebsiclla pJlcjajoiojUAS
CitrobacAcr, JTcjuidii
AjLhimiIO_b_a£lcjL JJDL.
Shi^clla dyscnieriac
Proteus vulgarls
Scrratia odorifcra
Times Isolated
73
27
La g
1 5
2
4
I 12
2 1
1
3
2
4
% Times
Isolated
42.7
15.8
5.3
8.8
1.2
2.3
7.0
12.3
0.6
1.8
1.2
2.3
% Recovery on Fecal ColifonnJSlcdk
m-FC
30.1
63.0
22.2
26.7
50.0
25.0
20.0
29.0
100.0
m-T7 MPN (EC Broth)
42.7 27.4
37.0 37.0
44.5 35.3
26.7 46.7
50.0
75.0
80.0
35.7
100.0
100.0
100.0
TOTAL - 170
Unidentifiable by the API-20E System for the identification of enterobacteriaceae (Analytab)
-------
K7
Table 20. Bacterial Species
from Chlorinated Effluents.
Number of % Times
Species Times Isolated Isolated
liicJiiiitiiia toJU
KJcjjslcUa pjQfemicaojuac
Pscudomopas. sp.
Unidentified*
Citrobacicr frcundii
Entcrobactcr cloacae
22
3
6
3
2
5
61.1
8.3
16.7
8.3
5.6
13.9
% Recovery on Fecal Conform Media
m-FC m T7 MPN (EC Broth)
27.3 50.0 22.7
33.4 66.6
94.4 5.6
66.6 33.4
100.0
100.0
TOTAL = 41
•"Unidentifiable by the API-20E System for the identification of enterobacteriaceae (Analytab)
-------
KH
Figure 1. Study Sites.
1 = La Plata Water Treatment Plant (Bayamdn)
2 = Los Filtros Treatment Plant (Guaynabo)
3 = Villalba Urban System Plant (Villalba)
4 = Ranchera Ward Treatment Plant (Yauco)
-------
-------
Figure 2, Total Coliform Densities found in Bayamon Influent Waters (September 24, 1987 to March 7.
1988).
-------
104
£
o
o
10
3 _
ID
LJL
O
10
2 .
CO
c
0)
Q
10' •:
10'
MPNBi
m-Endo Bi
0
468
Time (weeks)
12
14
-------
Figure 3. Total Coliform Densities in Guaynabo Influent Waters (September 24. 1987 to March 7, 1988).
-------
6 8 1
Time (weeks)
12
14
-------
Figure 4. Total Coliform Densities in Yauco Influent Waters (September 24, 1987 lo March 7, 1988).
-------
10'
o
ioH
ID
LJL
O
CD
C
-------
Figure 5. Total Coliform Densities in Villalba Influent Water (September 24, 1987 to March 7, 1988).
-------
10'
o
o
10*:
o^
>»
c
Q
10J :
10'
Time (weeks)
-------
Figure 6. Fecal Coliform Levels in Bayam6n Influent Waters (September 24, 1987 to March 7, 1988).
-------
10
Time (Weeks)
-------
. . . r -- 24 1087 lo March 7, 1988).
Figure 7. Fecal Coliform Densiues in Gu.
-------
10
E
o
o
ID
LL.
O
10
3 J
>» 10
c
-------
Figure 8. Fecal Coliform Densities in VillaJba Influent Waters (September 24, 1987 to March 7, 1988).
-------
£
o
o
1—
5
UL
O
10
3 .
10
2 _
c
0>
Q
101 -:
10°
m-FC
----O— m-T7
-a— MPN
0
—r~
2
8
10
12
14
Time (Weeks)
-------
Figure 9. Fecal Coliform Densities in Yauco Influent Waters (September 24, 1987 to March 1, 1988).
-------
10
0
Time (Weeks)
-------
Figure 10. Fec&l Streptococci Levels in Villalba Effluent Waters (September 24, 1987 io March 7, 1988).
-------
10
E
o
Y—
ID
LJL
O
CD
d
CD
Q
10°
10
-1
0
T-
2
-1 f
6810
Time (Weeks)
12
14
-------
Figure 11. Influent AODC* Densities at all Sites (September 24, 1987 to March 7. 1988). 'Acridine Orange
Direct Counts.
-------
10'
(1)
0)10'
Q
101
0
Bayam6n
Guaynabo
Villalba
Yauco
6810
Time (Weeks)
12
14
-------
99
APPENDIX I
ANOVA of Influent Water Quality Parameters by Site.
Parameter F df P_
Temperature
pH
Turbidity
Total Chlorine
Free Chlorine
9.00
3.68
14.58
1.00
1.00
3
3
3
3
3
, 40
, 40
, 40
, 40
, 40
<0.001*
<0,05*
<0.001*
>0.05"
>0.05
'Significant
-------
100
APPENDIX II
ANOVA of Effluent Water Quality Parameters by Site.
Parameter F df
Temperature
pH
Turbidity
Total chlorine
Free chlorine
24.25
2.28
4.24
16.63
27.04
3
3
3
3
3
, 40
, 40
, 40
, 40
, 40
<0.001*
>0.05
<0.05*
<0.001*
<0.001*
*Significant
-------
101
APPENDIX III
ANOVA of Influent Viable Count Methods by Site.
Method F df
MF-Total coliforms
MF-Fecal coliforms (m-FC)
MF-Fecal coliforms (m-T7)
MF-Fecal streptococci
MPN-Total coliforms
MPN-Fecal coliforms
MPN-Fecal streptococci
5.08
4.75
1.56
2.82
11.39
15.68
16.57
3
3
3
3
3
3
3
, 40
, 40
, 40
, 40
, 40
, 40
, 40
<0.05*
<0,05*
>0.05
>0.05
«acoi*
«0.001*
«0.001*
* Significant
-------
102
APPENDIX IV
ANOVA of Effluent Viable Count Results by Site.
Method F df
MF-Total coliforms
MF-Fecal coliforms (m-FC)
MF-Fecal coliforms (m-T7)
MF-Fecal streptococci
MPN-Total coliforms
MPN- Fecal coliforms
MPN-Fecal streptococci
1.01
0.89
0.51
0.84
1.43
0.69
0.80
3
3
3
3
3
3
3
, 40
. 40
, 40
, 40
, 40
, 40
, 40
>0.05
>0.05
>0.05
>0.05
>0.05
>0.05
>0.05
*Significant
-------
103
APPENDIX V
ANOVA of Methods; Pooling Sites,
INFLUENTS
MF -
MF -
MF -
MF -
MPN
MPN
MPN
Method
m-endo agar
m-FC agar
m-T7 agar
m-KF agar
- total coliforms
- fecal coliforms
- fecal streptococci
F
6.870
7.220
2.840
0.003
52.75
66.100
46.600
df
3, 44
3, 44
3, 30
3, 44
3, 44
3, 44
3, 44
P
<0.001*
<0.0005*
>0.05
>0.25
<0.0005*
<0.0005*
<0.0005*
EFFLUENTS
MF -
MF -
MF -
\fF -
MPN
MPN
MPN
Method
m-endo agar
m-FC agar
m-T7 agar
m-KF agar
- total coliforms
- fecal coliforms
- fecal streptococci
F
0.057
1.370
0.930
0.545
21.690
11.310
2.760
df
3, 44
3, 44
3, 30
3, 44
3, 44
3, 44
3, 44
P
>0.25
>0.25
>0.25
>0,25
<0.0005*
<0.0005*
<0.05
'significant
------- |