United States
Environmental Protection
Agency
Office of
Research and Development
Washington, DC 20460
EPA/600/6-91/006
July 1991
Preliminary Risk
Assessment for
Bacteria in Municipal
Sewage Sludge
Applied to Land
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EPA/600/6-91/006
July 1991
PRELIMINARY RISK ASSESSMENT FOR BACTERIA
IN MUNICIPAL SEWAGE SLUDGE APPLIED TO LAND
Environmental Criteria and Assessment Office
Office of Health and Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
Printed on Recycled Paper
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
n
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PREFACE
Section 405 of the Clean Water Act requires the U.S. Environmental Protection
Agency to develop and issue regulations that identify: (1) uses for sludge including disposal;
(2) specific factors (including costs) to be taken into account in determining the measures
and practices applicable for each use or disposal; and (3) concentrations of pollutants that
interfere with each use or disposal. To comply with this mandate, the U.S. EPA has
embarked on a program to develop four major technical regulations: land application,
including distribution and marketing; landfilling; incineration and surface disposal. The
development of these technical regulations requires a consideration of pathogens as well as
chemical constituents of sludge. Public concern related to the reuse and disposal of
municipal sludge often focuses on the issue of pathogenic organisms.
This report is one of a series whose purpose is to use the methodology described in
Pathogen Risk Assessment for Land Application of Municipal Sludge to develop preliminary
assessments of risk to human health posed by parasites, bacteria and viruses in municipal
sewage sludge applied to land as fertilizer or soil conditioner. The preliminary risk
assessment includes a description of the most critical data gaps that must be filled before
development of a definitive risk assessment and recommends research priorities.
m
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DOCUMENT DEVELOPMENT
Cynthia Sonich-Mullin, Project Officer
Norman E. Kowal, Technical Project Manager
Environmental Criteria and Assessment Office
U.S. Environmental Protection Agency
Cincinnati, OH
Authors
Marialice Wilson, Project Manager
Charles T. Hadden
Mary C. Gibson
Science Applications International Corporation
Oak Ridge, TN
Reviewers
Joseph B. Farrell
Risk Reduction Engineering Laboratory
U.S. Environmental Protection Agency
Cincinnati, OH
Matthew Lorber
Environmental Engineer
Exposure Assessment Methods Branch
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
Gerard N. Stelma, Jr.
Chief, Bacteriology Branch
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
Cincinnati, OH
Judith Olsen, Editorial Review
Environmental Criteria and Assessment Office
U.S. Environmental Protection Agency
Cincinnati, OH
IV
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TABLE OF CONTENTS
1. EXECUTIVE SUMMARY 1-1
2. INTRODUCTION . . 2-1
3. LITERATURE REVIEW OF BACTERIA 3-1
3.1. SIGNIFICANCE OF PATHOGENIC BACTERIA 3-1
3.1.1. Occurrence of Bacteria in Sludge 3-3
3.1.2. Transmission/Exposure Routes 3-3
3.1.3. Infective Dose 3-6
3.1.4. Epidemiology 3-9
3.2. SURVIVAL OF BACTERIA IN TREATED SLUDGE 3-11
3.2.1. Survival in Treatment Processes 3-12
3.2.2. Density of Bacteria in Treated Sludge 3-13
3.3. VIABILITY AND SURVIVABILITY OF BACTERIA 3-24
3.3.1. Survival in Soil 3-24
3.3.2. Survival in Water 3-33
3.3.3. Survival in Aerosols 3-37
3.3.4. Survival in Agricultural Products 3-41
3.4. TRANSPORT ... .... 3-45
3.4.1. Transport in Soil 3-45
3.4.2. Transport in Surface Runoff 3-50
3.4.3. Transport in Groundwater 3-52
3.4.4. Transport in Air ........ . ...... . ............... 3-53
4. PARAMETERS FOR MODEL RUNS 4-1
4.1. RATIONALE FOR PARAMETER SELECTION .............. 4-1
4.2. PARAMETER VALUES . . . ... . . .... ... . . .... . . . . 4-4
4.2.1. Main Program Parameters 4-7
4.2.2. Parameters for Subroutine RISK . 4-7
4.2.3. Parameters for Subroutine GRDWTR 4-7
4.2.4. Parameters for Subroutine RAINS 4-26
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TABLE OF CONTENTS (continued)
5. SITES FOR MODEL RUNS 5-1
5.1. SITE 1: ANDERSON COUNTY, TN 5-1
5.1.1. Description of Soil • 5-1
5.1.2. Narrative Climatologic Summary 5-2
5.1.3. Temperature • 5-2
5.1.4. Rainfall • • • • • 5-3
5.1.5. Parameters for Subroutine RAINS 5-3
5.2. SITE 2: CRAVES COUNTY, NM 5-4
5.2.1. Description of Soil . • 5-4
5.2.2. Narrative Climatologic Summary 5-5
5.2.3. Temperature . . . 5-5
5.2.4. Rainfall 5-5
5.2.5. Parameters for Subroutine RAINS 5-6
5.3. SITE 3: CLINTON COUNTY, IA , 5-6
5.3.1. Description of Soil • • • • 5-6
5.3.2. Narrative Climatologic Summary 5-7
5.3.3. Temperature . . . . • • • • 5-7
5.3.4. Rainfall . . . . ...;.. 5-7
5.3.5. Parameters for Subroutine RAINS :7. -.. 5-8
5.4. SITE 4: HIGHLANDS COUNTY, FL 5-8
5.4.1. Description of Soil 5-9
5.4.2. Narrative Climatologic Summary . . 5-9
5.4.3. Temperature . . 5-9
5.4.4. Rainfall • 5-10
5.4.5. Parameters for Subroutine RAINS 5-10
5.5. SITE 5: KERN COUNTY, CA 5-11
5.5.1. Description of Soil .-...' • 5-11
5.5.2. Narrative Climatologic Summary 5-11
5.5.3. Temperature 5-11
5.5.4. Rainfall • • • 5-12
5.5.5. Parameters for Subroutine RAINS 5-12
VI
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TABLE OF CONTENTS (continued)
5.6. SITE 6: YAKIMA COUNTY, WA 5-12
5.6.1. Description of Soil 5-13
5.6.2. Narrative Climatologic Summary .,;..... .f...;;...., 5.13
5.6.3. Temperature 5-13
5.6.4. Rainfall . . . ;...-, . . ... . . . . . '. 5-14
5.6.5. Parameters for Subroutine RAINS .... .-.' 5-14
6. RESULTS .;-..;. . . . ... 6_i
6.1. SENSITIVITY TO VARIABLES 6-1
6.2. EXPOSURE COMPARTMENTS . . v,..,.,...... '. ......... 6-27
6.2.1. . ONSITE -.. . . ... . . -.-; ...-• 6-27
6.2.2. OFFSITE ................ -./....;.-,; ; 6-27
6.2.3. Food Consumer (EATER) , 6-28
6.2.4. Groundwater Drinker (DRINKER) ...... ' 6-28
6.2.5. SWIMMER ........;.; ....... ,6-28
7. CONCLUSIONS . . .............. ,...;., . 7-1
7.1. EXPOSURE FACTORS ,. . . ,. > 7-2
7.2. INFECTIVE DOSE .... ; ....... ' 7_2
7.3. SUBSURFACE TRANSPORT ..............';].'.'. '.'."" 7-2
7.4. SURFACE RUNOFF AND SEDIMENT TRANSPORT, .. 7-3
7.5. OFFSITE AEROSOLS v. . . . . .,. ........ ' 7.4
7.6. WAITING PERIOD , '.'..'.'.'.'.'.'.','. 7-4
8. RESEARCH NEEDS ... ... 8-1
8.1. INFORMATION NEEDS FOR BACTERIA ............ 8-1
8.2. MODEL DEVELOPMENT . . , . .... . . . . . . | g-2
9. REFERENCES . . ; . . . . .,; ';. . ... ..-.-. ."/. . . . _ 9.1
APPENDIX A. Model Overview .:.... . . .;... . . ,. ... . A-l
APPENDIX B. Maximum Probability of Infection, Site 1, Practice I , . B-l
APPENDIX C. Sample Maximum and Cumulative Probability of Infection and
Maximum Contents of Compartments . C-l
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LIST OF TABLES
No. Title Page
2-1 Compartments Included in the Sludge Management Practices .......... 2-2
2-2 Sludge Management Practices and Descriptions in Pathogen Risk
Assessment Model 2-4
3-1 Computer Search Strategy 3-2
' • '-:''.'
3-2 Pathogenic Bacteria of Major Concern in Sewage Sludges 3-4
3-3 Pathogenic Bacteria of Minor Concern in Sewage Sludges 3-5
3-4 Infective Dose of Bacteria to Humans 3-8
3-5 Bacterial Densities in Treated Sludge 3-14
3-6 Bacterial Die-Off in Soil 3-25
3-7 Bacterial Die-Off in Aquatic Systems 3-34
3-8 Bacterial Die-Off in Aerosols 3-39
3-9 Bacterial Transport 3-51
4-1 Test Values of Parameters for Die-Off Equations 4-5
4-2 Values of Temperature Parameters 4-6
4-3 Main Program Parameters ......' 4-8
4-4 Input Parameters for Effect of Irrigation and Crop Type on Probability
of Infection 4-21
4-5 Input Parameters for Garden Crop Type 4^22
4-6 Values of Cattle Feeding Parameters 4-23
4-7 Parameters for Subroutine RISK 4-24
vin
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LIST OF TABLES (continued)
No. Title Page
4-8 Parameters for Subroutine GRDWTR . /. . . .".'.;. V 4-25
4-9 Parameters for Subroutine RAINS 4-27
6-1 Effect of Main Program Parameters on Probability of Infection 6-2
6-2 Process Functions and Their Effect on Exposure 6-15
6-3 Maximum Probability of Infection by Site and Practice 6-17
6-4 Sensitivity Coefficients of Site-Specific Variables 6-19
6-5 Sensitivity of Subroutine RAINS to Input Parameters 6-26
IX
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LIST OF FIGURES
NQ, Title
4-1 Effect of Slope/Intercept on Die-Off
6-1 Effect of MID on Infection Probability
6-2 Effect of MID on ONSITE Infection . . . ;
6-3 Effect of MID on DRINKER Infection
6-4 Effect of MID on SWIMMER Infection
Page
4-3
6-21
6-22
6^23
6-24
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ABBREVIATIONS AND SYMBOLS
CPU Colony forming units
D&M Distribution and marketing
dia Diameter'
g Gram
ha Hectare
hr Hour
ID Infective dose
MID Minimum infective dose
min Minute
MPN Most probable number
NOAA National Oceanic and Atmospheric Administration
PFRP Processes to Further Reduce Pathogens
PSRP Processes to Significantly Reduce Pathogens
sec Second
USDA U.S. Department of Agriculture
wt Weight
XI
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1. EXECUTIVE SUMMARY
This preliminary risk assessment study focuses on the probability of human infection
from enteric bacterial pathogens in municipal sludge applied to land. It is based on the
Pathogen Risk Assessment computer model and methodology described in Pathogen Risk
Assessment for Land Application of Municipal Sludge (U.S. EPA, 1989a).
This document reports the results of a literature review designed to find the data on
pathogenic bacteria required by the pathogens methodology, and the results of numerous
site-specific computer simulations, running the Pathogen Risk Assessment Model with a
wide range of values for the parameters required. The parameters required for bacteria are
(1) minimum infective dose; (2) density of viable bacteria in treated sludge destined for land
application; (3) die-off rates in soil, dry particulates, liquid aerosols and water; and (4)
dispersion in the environment, i.e., transport in water, soil and air.
Six sites were chosen to provide diversity in geographic location, topography, soil
type, rainfall pattern and temperature. Locations selected for site-specific application of the
model include Anderson County, TN; Chaves County, NM; Clinton County, LA; Highlands
County, FL; Kern County, CA; and Yakima County, WA.
An initial sensitivity analysis was performed using site-specific parameters for Site 1,
Anderson County, TN. Main program variables used in the model run were varied over a
range of values to determine the sensitivity of the model to variations in conditions. In
general, the default value of a given parameter was compared to a reasonable higher and
a reasonable lower value, where the high and low values were taken from available
literature or estimated when literature values were not available.
In this analysis, it is assumed that bacteria are transported into subsurface soil and
subsequently into groundwater and are included in any droplet aerosols formed by spray
application, as well as in any particulate aerosols formed by disturbance of the soil by wind
or by cultivation. It is also assumed that the bacteria die at a characteristic rate that
depends on the ambient temperature and the medium in which they are found.
The risk of infection from bacterial pathogens in treated sewage sludge appears to
be small when judged by model results, but there are a number of factors the model does
1-1
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not address, including regrowth in composted or D&M sludge. The model runs indicated
that significant exposures are likely only if the number of organisms is very high, either
because the concentration of bacteria in the treated sludge is unrealistically high or because
a high application rate is used. Significant exposures occurred only onsite, either by direct
contact or by swimming in a pond containing runoff. However, runoff and surface transport
of bacterial pathogens to the onsite pond do not appear to present a major health risk.
Exposure by direct contact immediately after sludge application could be a source of
infection, although the risk of infection decreases very rapidly, so that the cumulative risk
of infection from a single application is typically only slightly higher than the maximum daily
risk. The results suggest that if the infective dose is >20, the probability of infection
becomes minimal.
The results of the model runs clearly indicate that the highest risk of infection should
occur during and immediately after application of the sludge. Die-off and dilution by soil
should subsequently reduce the number of infectious organisms very rapidly. Aside from
the expected dependence of exposure on total numbers, of pathogens present and the
infectious dose, the most significant effects on exposure appear to be related to die-off rates
and to dry particulate aerosol formation. Fractional transfers of pathogens from soil to
subsoil and to soil surface water were also significant, as was the volume of the onsite pond
in which the contaminated soil surface water was diluted.
The results described above do not support the requirement for an extended waiting
period before use of sludge-amended soils. Bacterial concentrations in all of the exposure
media decreased so rapidly that a waiting period of at most a few days should be sufficiently
protective. However, compost and D&M sludge products designed for use in the home
garden can allow multiplication or regrowth of bacteria, such as Salmonella, resulting in
extremely variable pathogen densities and the possibility of a higher dose of pathogens upon
ingestion of crops.
The following information is needed to improve the usefulness of the Pathogen Risk
Assessment Model and to allow for more reliable risk assessment of land application of
sewage sludge:
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• Simple and accurate standardized methods for quantifying, by species and
strain, pathogenic bacteria in treated sludge destined for land application, in
final distributed and marketed (D&M) sludge products, and in environmental
media;
• Improved understanding of minimum infective doses, particularly low-dose
effects and MIDs for sensitive subjects;
• Additional information on regrowth of bacteria in compost and D&M sludge,
including factors enhancing or limiting regrowth;
• More accurate survival and transport data on all pathogenic bacteria of major
concern in sludge, especially retardation coefficients for transport of bacteria
in saturated soil;
• Development of an index of soil types that would correlate capacity for solute
transport and suitability for sludge application (also valuable for onsite waste
disposal or solid waste disposal);
• Research on subsurface injection of sludge and the relative probability of
bacterial transport in groundwater; and
• Epidemiologic studies evaluating whether there is a correlation between
bacterial infections (not necessarily disease) and bacterial aerosols.
Future modifications of the Pathogen Risk Assessment Model that may improve its
accuracy include the following:
• The model should be changed to consider runoff of pathogens from
unincorporated sludge when rainfall occurs in the first 24 hours.
• A transfer factor could be added to the model to allow for redistribution of
pathogens from subsurface to surface soil when the field is plowed.
• For a better description of sludge use on public parks and golf courses, which
are more likely to have ponds, it might be beneficial to add the option for
existence of a pond onsite.
• The limits of Subroutine RAINS should be further characterized to establish
operating boundaries for input variables.
1-3
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2. INTRODUCTION
This preliminary risk assessment study focuses on the probability of human infection
from enteric bacterial pathogens in municipal sludge applied to land. Sludge, a byproduct
of sewage treatment, is the mixture of solids and liquids remaining after treatment processes
remove solids from municipal or domestic wastewater. Secondary and tertiary sludges
contain biomass resulting from microbial digestion of the sewage. Being derived from
human sanitary wastes, sludge contains microorganisms that colonize humans and can cause
infection and disease.
This risk assessment is based on the Pathogen Risk Assessment computer model and
methodology described in the U.S. Environmental Protection Agency's (U.S. EPA's)
Pathogen Risk Assessment for Land Application of Municipal Sludge (U.S. EPA, 1989a).
Appendix A provides an overview of the model. The purpose of the model is to determine
the probability of infection of a human receptor from pathogens in land-applied sludge. The
model consists of a series of compartments (Table 2-1) representing discrete points in the
application pathway. The compartments are the various locations, states or activities in
which sludge or sludge-associated pathogens exist; they vary to some extent among practices.
Compartments representing sources of human exposure are designated with an asterisk in
Table 2-1. In each compartment, pathogens increase, decrease or remain the same in
number with time, as specified by "process functions" (growth, die-off or no population
changes) and "transfer functions" (movement between compartments). Infection rather than
disease is used to measure risk in the methodology, since exposures to pathogenic bacteria
may lead to no infection, human infection that is asymptomatic or subclinical (no illness),
or human infection with illness (Kowal, 1985). The outputs produced by running the model
are numerical values for the probability of a human receptor receiving an exposure
exceeding the minimum infective dose (MID) in a 24-hr period. The MID for humans may
vary from as few as 10-100 Shigella organisms to as many as 10s-108 Salmonella (Kowal,
1985). The model will run until the day specified or until the number of pathogens in each
compartment decreases to <1 at which point the number is rounded to zero.
2-1
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TABLE 2-1
Compartments Included in the Sludge Management Practices
Compartment
Name and Number
Application
Incorporation
Application/Tilling
Emissions
Soil Surface
Particulates
Surface Runoff
Direct Contact
Subsurface Soil
Groundwater
Irrigation Water
Soil Surface Water
Offsitc Well
Aerosols
Crop Surface
Harvesting
(Commercial) Crop
Animal Consumption
Meat
Manure
Milk
Hide
Udder
"Source: U.S. EPA, 1989a
Asterisk indicates exposure
Liquid Sludge Dried/Composted
Management Practices Sludge Management
Practices
1
1
2
3*b
4
5*
6*
7*
8
9
10
11
12*
13*
14
15
16*
compartments.
II
1
2
3*
4
5*
6*
7*
8
9
10
11
12*
13*
14
17
18*
19
20*
2-1
22
111 IV
1 1
2
3* 3*
4 4
5* 5*
6*
7* 7*
8 8
9
10
11 11
12*
13*
14 14
15 15
16*
17
18*
19
20* .
21
22
V
1
3*
4
5*
7*
8
11
14
2-2
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Two categories of land application are employed in the methodology: (1) agricultural
utilization and (2) distribution and marketing (D&M). The source of pathogenic bacteria
is either liquid or dried/composted municipal sewage sludge. The five municipal sewage-
sludge management practices (Table 2-2) included in the model are application of liquid
treated sludge (I) for production of commercial crops for human consumption, (II) to grazed
pastures, and (III) for production of crops processed before animal consumption; and
application of dried or composted sludge (IV) to residential vegetable gardens and (V) to
residential lawns. Practices III and V, while ostensibly limited to hay fields and residential
lawns, respectively, can be modified by selection of appropriate parameters to represent
sludge application on golf courses, reclaimed strip mines or logged sites, parks, roadsides,
etc. Although Practice V does not include an onsite pond, the risk to a human swimming
in a pond (SWIMMER) can be modeled by using appropriate parameters in Practice III.
Risk assessment for pathogens in land-applied municipal sludges requires the
following input data:
• Types of pathogens and their concentrations in the sludge, their survivabilities, and
their infective doses;
• The sludge reuse/disposal option used and the conditions of sludge application
(quantities, frequencies, application method);
• The fate of the pathogens in the environment, i.e., the die-off rate under different
conditions including moist soil, dry particulates, droplet aerosols and water; and
• The level of exposure of human receptors to the applied sludge.
This document reports the results of a literature review designed to find the data on
bacteria required by the pathogens methodology, and the results of numerous computer
simulations, i.e., running the Pathogen Risk Assessment Model with a wide range of values
for the parameters required. Six sites, chosen to provide diversity in geographic location,
topography, soil type, rainfall pattern and temperature, were selected for site-specific
applications of the model: Anderson County, TN; Chaves County, NM; Clinton County, IA;
Highlands County, FL; Kern County, CA; and Yakima County, WA. Because of the
unlimited number of possible sites, the final selections were somewhat arbitrary, being based
2-3
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TABLE 2-2
Sludge Management Practices and Descriptions in
Pathogen Risk Assessment Model
PRACTICE
DESCRIPTION15
II
III
IV
V
Application of Liquid Treated Sludge for Production of
Commercial Crops for Human Consumption
Application of Liquid Treated Sludge to Grazed Pastures
Application of Liquid Treated Sludge for Production of
* Crops Processed before Animal Consumption
>•.' ' : .' " ' . < . • •"'•.. i ,. ,' ..'•.-,
Application of Dried or Composted Sludge to Residential
Vegetable Gardens
Application of Dried or Composted Sludge to Residential
Lawns
"Source: U.S. EPA, 1989a ^ . , • -
'Two types of sludge are used in this model - liquid and dried/composted. The extent
of treatment or conditioning prior to application is variable and must be determined
for each case.
;''- 2-4
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on an attempt to represent different geographic regions and to ensure a variety of weather
patterns.
Exposure pathways, i.e., migration routes of parasites from or within the application
site to a receptor, for sludge applied to land include the following:
• Inhalation and ingestion of emissions from application of sludge or tilling of
sludge/soil;
• Inhalation and ingestion of windblown or mechanically generated particulates;
• Swimming in a pond fed by surface water runoff;
• Direct contact with sludge-contaminated soil or crops (including grass,
vegetables, or forage crops);
• Drinking water from an offsite well;
• Inhalation and subsequent ingestion of aerosols from irrigation;
• Consumption of vegetables grown in sludge-amended soil;
• Consumption of meat or milk from cattle grazing on or consuming forage from
sludge-amended fields.
Because the focus of the model is enteric pathogens, this methodology assumes that
exposure to bacteria will not result in infection unless the organisms are actually swallowed.
Risks due to inhalation of enteric pathogens will be considered only because the organisms
can be subsequently swallowed. However, disease can result through routes of exposure
other than the alimentary tract; risks from such exposures can be modeled by choice of the
appropriate organism-specific parameter values.
The following human receptors are the exposed individuals whose probability of
infection by parasites is calculated by this model:
• Onsite person (ONSITE) who is exposed by ingestion (includes pica in
children) of soil, vegetables or forage or by inhalation and subsequent ingestion
of aerosols (particulates or liquid);
« Offsite person (OFFSITE) who is exposed to particulate or liquid aerosols
carried by wind;
• Food consumer (EATER) who eats vegetable crops, meat or milk produced on
sludge-amended soil;
2-5
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• Groundwater drinker (DRINKER) who consumes water from a well near but
not on the sludge application site;
• Pond swimmer (SWIMMER) who ingests a small amount of water while
swimming in the pond that receives the surface runoff from the application site.
The model conceptualization (U.S. EPA, 1989a) specifies that workers engaged in the
transportation, handling and application of liquid sludge are not included as exposed
individuals because such activity is an occupational exposure.
The U.S. EPA (1986, 1988) has provided extensive information relevant to the
conceptual risk assessment framework for land application of sludge. These key studies
address the pathogens associated with sewage sludge, as well as exposure pathways and the
potential risks to humans from each of the pathways. Most of that information will not be
repeated here. Additional information about the computer model and methodology is
available in Volumes I and II of Pathogen Risk Assessment for Land Application of Municipal
Sludge (U.S. EPA, 1989a) and in Wilson et al. (1989); a brief overview of the model is
included as Appendix A.
2-6
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3. LITERATURE REVIEW OF BACTERIA
i .,v •'".••;•.,'.•• • • - •
A literature search was performed to find the most current information available for
the parameters required by the model for simulating land application of sludge. This
literature was reviewed to find published data on which to base the ranges of values for
those parameters. The parameters required for bacteria are (1) minimum infective dose;
(2) density of viable bacteria in treated sludge destined for land application; (3) die-off rates
in soil, dry particulates, liquid aerosols and water; and (4) dispersion in the environment,
i.e., transport in air, soil and water.
Appropriate codes and keyword truncation were used to produce the most effective
search strategy for query of each data base. Table 3-1 lists the computerized data bases
queried and the keywords used. The three columns of keywords were "anded" together to
produce a set in which at least one keyword in each column was a descriptor or was
contained in a retrieved record.
References in reviews and in relevant articles retrieved by the computer search were
also evaluated, and names of pertinent authors were searched to find recent papers.
Because the scope of this literature review is limited to information satisfying the
parameters required, by the model, the reader is directed to references cited for more
comprehensive background information.
3.1. SIGNIFICANCE OF PATHOGENIC BACTERIA
The presence of pathogenic bacteria in sewage sludge has been well-researched and
documented. Several reviews include information on specific types of bacteria present in
municipal sewage, their survival and density in sludge, their pathogenicity, and potential
health risks associated with land application (WHO, 1981; Kowal and Pahren, 1982; Kowal,
1985; U.S. EPA, 1986,1988; Lund, 1978; Burge and Marsh, 1978; Pedersen, 1981; Elliot and
Ellis, 1977; Feachem et al., 1983).
The word enteric (relating to the intestines or, more generally, the alimentary tract)
refers to the fact that the natural habitat of these bacteria is the intestinal tract of animals
and humans (Domingue, 1983). Enteric bacteria is the general term used to refer to the
' 3-1
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TABLE 3-1
Computer Search Strategy
Data Bases
Keyword Groups
AGRICOLA
AGRIS
BIOSIS
CAB ABSTRACTS
CRIS/USDA
ENVIROLINE
FSTA
NTIS
POLLUTION ABSTRACTS
TOXLINE
WATER RESOUR. ABS
ZOOLOGICAL RECORD
BACTERIA
SALMONELLA
F(A)ECAL COLIFORMS
STREPTOCOCCI
STAPHYLOCOCCI
YERSINIA
VIBRIO CHOLERAE
ENTEROCOCCI
SHIGELLA
CAMPYLOBACTER
ESCHERICHIA COLI
LEPTOSPIRA
LISTERIA
SURVIVAL
DISPOSAL
TRANSPORT
FATE
VIABILITY/
VIABLE
DIE-OFF
MOVEMENT
REGROWTH
SEWAGE
SOIL
AIR
AEROSOL
WATER
SLUDGE
GROUND-
WATER
3-2
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organisms within the families Enterobacteriaceae and Vibrionaceae. Although almost any
organism in these two families can cause illness in humans whose host defenses have been
compromised (opportunistic pathogens), the focus of this review is those enteric pathogens
likely to cause disease because of transmission in municipal sewage sludge.
This literature review does not address all the pathogens of minor concern; however,
Listeria monocytogenes is a representative of the class of opportunistic pathogens that can
pose a health threat to certain groups, e.g., pregnant women and their fetuses, newborns and
immunologically compromised persons. L. monocytogenes occurs in sewage and sewage
sludge and survives treatment processes and land application even longer than Salmonellae,
and listeriosis is transmitted in food. For these reasons, L. monocytogenes will be considered
in this review as an example of opportunistic pathogens, which may constitute a health risk
for certain individuals as a result of land application of sludge.
Although the frequency of enteric bacterial disease attributable to sewage sludge is
low, increased use of sludge for land application could increase the risk. Table 3-2 lists
those enteric bacterial pathogens of niajor concern, and Table 3-3 lists those pathogens of
minor concern. Kowal (1985), Feachem et al. (1983) and Domingue (1983) review the
information on these pathogens and the diseases they cause.
3.1.1. Occurrence of Bacteria in Sludge. It has been extensively documented that
pathogenic enteric bacteria are present in sewage and concentrated in sludge (Elliott and
Ellis, 1977; Pedersen, 1981; Kowal, 1985; Pahren, 1987; U.S. EPA, 1988), and Section 3.2.2
summarizes information on the density of bacteria in treated sludge. An extensive review
of the occurrence, survival, transmission routes and epidemiology of the specific bacteria of
niajor concern is presented in Feachem et ah (1983). Boardman et al. (1989) reviewed the
occurrence, distribution, detection and persistence of waterborne bacterial pathogens and
provided epidemiologic information on some of the bacteria of major concern including
Campylobacter, Salmonella, Shigella, Vibrio, Leptospira and Yersinia.
3.1.2. Transmission/Exposure Routes. Transmission of most of the enteric bacteria is by
the fecal-oral route, with water- and food-borne outbreaks being of prime importance. As
shown in Table 3-2, some bacteria persist as reservoirs in infected animals, and there are
some instances in which person-to-person transmission is of importance.
3-3
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TABLE 3-2
Pathogenic Bacteria of Major Concern in Sewage Sludges
Organism
Disease
Nonhuman Reservoir
Campylobacter jejuni
Escherichla colt
(pathogenic strains)
Leptospira spp.
Salmonella paratyphi A,B, C
Salmonella typhi
Salmonella spp.
Shigella sonnet, S. flexneri, S.
boydii, S. dysenteriae
Vibrio cholerae
Yersinia enterocolitica,
Y, pseudotuberculosis
Gastroenteritis
Gastroenteritis
Leptospirosis '
Paratyphoid fever
Typhoid fever
Salmonellosis
Shigellosis (bacillary dysentery)
Cholera
Yersiniosis
Cattle, dogs, cats, poultry
Domestic and wild mammals,
rats
Domestic and wild mammals,
birds, turtles
Domestic and wild birds and
mammals
'Source: Kowal, 1985; U.S. EPA, 1988; Domingue, 1983
3-4
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TABLE 3-3
Pathogenic Bacteria of Minor Concern in Sewage Sludges
Aeromonas spp.
Bacillus aureus
Brucella spp.
Citrobacter spp.
Clostridium perfringens
Coxiella bumetii
Enterobacter spp.
Erysipelothrix rhusiopathiae
Francisella tularensis
Klebsiella spp.
Legionella pneumophila
Listeria monocytogenes
Mycobacterium tuberculosis
Mycobacterium spp.
Proteus spp.
Pseudomonas aeruginosa
Serratia spp.
Staphylococcus aureus
Streptococcus spp.
'Source: Kowal, 1985
'3-5
-------
Listeria monocytogen.es has caused several outbreaks of foodborne listeriosis, one of
which was caused by cabbage grown in fields fertilized with compost and manure from sheep
known to be infected (Farber and Losos, 1988). The susceptibility of pregnant women and
immunocompromised people to listeriosis, the known transmission of the disease in foods,
the considerable reservoir of L. monocytogenes in human and animal populations, and the
persistence of the pathogen in treated sewage sludge (Watkins and Sleath, 1981) raises the
possibility that application of sewage sludge or compost to gardens, and consequently to
leafy vegetables, could be a pathway for disease transmission.
Levine et al. (1990) evaluated the exposure routes for waterborne illnesses and found
bacterial diseases associated with drinking untreated groundwater; drinking from treatment-
deficient or distribution-deficient water supplies; or unintentionally ingesting surface water
while swimming. Shigellosis caused by drinking water contaminated with human waste can,
in turn, lead to secondary transmission in campgrounds, day-care centers, and similar settings
(Levine et al., 1990) because of the low infective dose required to allow shigellae to spread
by contact.
During the period 1986-1988, Shigella sonnei was the most commonly reported
bacterial pathogen causing waterborne disease in the United States (Levine et al., 1990).
Four outbreaks of shigellosis affecting 2733 people were reported, with each instance arising
from drinking water contaminated with human waste. Shigella has also caused outbreaks
from unintentional ingestion associated with swimming. Salmonella-was, the etiologic agent
in two outbreaks affecting 70 persons, and Campylobacter caused illness in 250 cases
connected with one outbreak during the period 1986-1988.
3.1.3. Infective Dose. The ability of enteric pathogenic bacteria to cause infection in the
human who ingests them depends on the virulence or infectivity of the species or strain and
the susceptibility of the human receptor. Consequently, infection is a dose-response
relationship in which the dose is the number of viable bacterial cells to which the human
is exposed and the response is the level of infection, that is, none, subclinical infection with
no disease, or infection with disease (Kowal, 1985).
3-6
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The MID, or minimum infective dose, is generally considered to be the dose that will
infect 50% of the population (U.S. EPA, 1988) and is also known as the median infective
dose or ID50 (Blaser and Newman, 1982; Feachem et al., 1983). Estimates of MIDs for
bacteria vary appreciably depending on several important factors: route of.exposure; timing
of exposure, e.g., acute or chronic; resistance mechanisms of the host, including immune
responses or barriers to infection such as stomach acidity or leukocyte activity; general
health and age of the host; treatment with antibiotics, which reduces competition and thus
the number of bacteria required to cause infection; and virulence of the strain or serotype
of bacteria. Therefore, a conservative approach that takes into account sensitive.individuals
is probably a better choice for infective dose than ID^ for a model that evaluates risk from
pathogenic microorganisms to the human receptor who is most likely to be exposed.
In the methodology and computer model used in this preliminary risk assessment, the
default MID value for Salmonella bacteria is 101 organisms. This selection is based on the
premise that, to be conservative, such an estimate would apply to a sensitive individual.
However, in selecting an MID for use in the model, the user should rely on the best
information available for the specific organism in question. Table 3-4 presents a summary
of doses of bacteria found,to be infective to humans and illustrates an incremental dose-
response relationship.
Kowal (1985) and Pahren (1987) reviewed information on oral infective doses of
bacteria. Table 3-4 suggests that perhaps 103 is a conservative infective dose since neither
infection nor illness occurred at that level. Blaser and Newman (1982) concluded, however,
that the infective dose for Salmonella may be fewer than 10^ bacteria. In their review of 11
outbreaks of salmonellosis, 6 outbreaks resulted from ingested doses calculated to be <103
organisms. In a 1965 salmonellosis outbreak, infection may have resulted from as few as
17 organisms (Boring et al., 1971); however, Blaser and Newman ,(1982) point out several
factors that may have affected that infective dose estimate. D'Aoust and Pivnick (1976) also
suggest that there is evidence of salmonellosis being initiated by as few as 10-100 cells.
Blaser and Newman (1982) conclude that, enteric bacteria for which hurnans are the only
host (host-adapted) may have lower infective doses than non-adapted strains. They also
note that few data have been available on effects of low doses of Salmonellae and that for
3-7
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TABLE 3-4
Infective Dose of Bacteria to Humans8
Bacterium
Clostridium
perfringens
Escherichia coli
Several strains
Salmonella typhi
Most strains
S. newport
S. bareilly
S. anatutn
S. meleagridis
S, derby
S. pullorum
Shigella
dysenteriae
Shigella jlexneri
Streptococcus
faecalis var.
liquefaciens
Vibrio cholerae
NaHCO3-buffered
unbuffered
No
Infection
No
Illness
104
103
104-106
104-106
KrMO6
104-109
108
10
104-1010
Infection
Without
Illness
104-106
Percent of Volunteers Developing Illness
1-25.
106
105
10s
10s
IC^-IO8
106
10-102
102-104
109
26-50
108b
108
K^-IO8
106
106
106
107
107
109
lO^lO4
1010
103-108
108-
1011
51-75
109
108-1010
107-108
103
loMo9
104-106
76-100
109
1010
108-109
109-1010
104
106-108
"Source: Kowal, 1985
bDoses gjven in number of bacteria.
3-8
-------
>90% of salmonellosis cases in the United States, information on vehicle and mode of
transmission is quite limited. Feachem et al. (1983) caution that infective dose studies
involving volunteers usually rely on infecting 50% of well-nourished adult subjects, a practice
that may not adequately represent risk of infections in a smaller percentage of more
sensitive individuals.
Kowal (1985) points out that Salmonella spp. and Shigella spp. probably pose the
greatest risk of infection since they are the most common bacterial pathogens found in
municipal wastewater. U.S. EPA (1988) cites minimum infectious doses for bacteria in the
range of lOMO8 organisms, concentrations present in some sludges. Metro (1983a) gives a
range of infectious dose of lO^lO5 for Salmonella. Keswick (1984) reported infectious doses
of 10a-102 for Shigella dysenteriae; 106-108 for Escherichia coli; and 106-108 for Vibrio cholerae.
Levine et al. (1973) reported illness in healthy volunteers from virulent (invasive toxigenic)
Shigella dysenteriae in doses as low as 101 organisms.
3.1.4. Epidemiology. The incidence of enteric bacterial Infections and disease is probably
much higher than reported. The Centers for Disease Control (CDC) reported "49,000 cases
of salmonellosis, >30,000 cases of shigellosis, >400 cases of typhoid fever, 54 cases of
leptospirosis and 8 cases of cholera in the United States during 1988 (CDC, 1989). The
number of reported cases of shigellosis in 1988 is " 10,000 more than was reported any year
since 1951, and there has been a steady increase in the incidence of salmonellosis over the
past 35 years. However, the estimated incidence of salmonellosis infection in the United
States per year approaches two million cases from ingestion of Salmonellae-contaminated
food or drink. Although there are a few reported instances of infection by direct contact
with or inhalation of infective Salmonella, these are rare (Domingue, 1983) and typically are
immunologically compromised individuals. The World Health Organization Working Group
(WHO, 1981) considered the major reservoirs of human salmonellosis in Great Britain to
be poultry, meat, and dairy products and the main reason for the spread of infection to be
inadequate food hygiene.
Irrigation of crops with untreated wastewater or sewage has been linked with
occurrence of salmonellosis, shigellosis, listeriosis and cholera, but there is little
epidemiologic evidence linking treated sludge application with enteric bacterial illness
3-9
-------
(Kowal, 1985; WHO, 1981; Farber and Losos, 1988). Geldreich and Bordner's review
(1971) cites several outbreaks of salmonellosis and typhoid linked to fruit and vegetable
contamination by sewage or sludge, but Feachem et al. (1983) note that bacterial
contamination risks can be minimized by discontinuing irrigation or fertilization with sludge
or wastewater at least two weeks before harvest, a practice that is not as conservative as the
stringent U.S. EPA proposed guidelines (U.S. EPA, 1989b). Burge and Marsh (1978) report
two instances in which spreading of sewage sludge on lawns was implicated in outbreaks of
salmonellosis in infants and children, but they conclude that there is little evidence to show
disease is spread by land application of treated sewage sludge and effluents. Reddy et al.
(1985) report a number of instances in which land application of sewage sludge resulted in
cases of human salmonellosis. To evaluate the human and animal health effects associated
with land application of treated sewage sludge at rates beneficial to agriculture and within
Ohio guidelines, Reddy et al. (1985) applied sludge (at 2-10 metric tons/ha) on eight farms.
They noted no significant health risk from Salmonella spp.
Anaerobically digested sludges known to contain Salmonellae were found to present
no apparent risk to farm families when used in agricultural applications (Ottolenghi and
Hamparian, 1987). Feachem et al. (1983) consider the role of sludge application to pastures
in transmitting salmonellosis infections as controversial, since there is evidence both for and
against the importance of pasture contamination in cattle salmonellosis. They do conclude,
however, that Salmonella survival on pasture is shorter than on soil, diminishing farther up
the blades of grass than near the ground surface.
Pahren (1987) concludes that the studies evaluating health effects associated with
handling or processing municipal solid waste showed "no proven adverse effects from the
microorganisms contained in the waste." Studies of aerosol-borne microorganisms in the
vicinity of wastewater treatment plants showed few if any significant adverse health effects
at low concentrations of bacteria.
Camann et al. (1980) monitored microorganisms in wastewater aerosols from a
wastewater treatment plant and correlated exposure levels with attendance of children at
a nearby school to determine whether there was an association of adverse health effects with
wastewater aerosols. Geometric mean concentrations in air 30-50 m downwind ranged from
3-10
-------
4.2 colony forming units (CFU)/m3 fecal streptococci to 12 CFU/m3 total coliforms and 19
CFU/m3 mycobacteria. The authors calculated that students were probably exposed to a
peak dose of 2 CPU of mycobacteria and 0.8 CPU of fecal streptococci. There was no
correlation of adverse health effect with this exposure level.
The preceding study was included in a symposium on the health effects of
microorganisms in wastewater aerosols (Pahren and Jakubowski, 1980). The epidemiologic
studies reported few adverse health effects from exposure to bacteria in aerosols. In a
summary of these studies, Pahren and Jakubowski (1981) explain why microorganisms in
wastewater aerosols from treatment plants did not cause more infection and disease among.
exposed persons. In addition to the fact that aerosol concentrations of particular pathogens
were low and diminished rapidly with increasing time and distance from the source, the
microorganisms in question were primarily enteric pathogens whereas the exposure route
was inhalation.
Although the Pathogen Risk Assessment Model assumes that inhaled organisms are
ultimately ingested, that assumption is an oversimplification required by the limitation on
model size. However, enteric bacteria, at the relatively low concentrations found in
aerosols, are unlikely to be a health threat because their access to ideal receptor sites is
minimized by the inhalation route and because of their rapid die-off in air (see Section
3.3.3).
3.2. SURVIVAL OF BACTERIA IN TREATED SLUDGE
The density of bacteria in municipal sewage sludge is site-specific, being related to
the population served by the sewage treatment system, i.e., source of the wastes, species of
bacteria present, geographic area, and season or climate. Density and viability of bacterial
species are also dependent on the type of sludge treatment. Primary sludge has received
primary treatment such as screening and settling; secondary sludge is produced by biologic
waste treatment, or secondary treatment; primary and secondary sludge are combined to
produce mixed sludge (U.S. EPA, 1988).
The goals of conventional sludge treatment processes are to lower the volatile solids
content and to stabilize the sludge. These sludge treatment processes are (1) anaerobic
3-11
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digestion, the microbiologic degradation of the organic matter in sludge in the absence of
oxygen; (2) aerobic digestion, the biochemical oxidation of organic matter; (3) composting,
the natural, aerobic microbiologic process of decomposing organic matter to produce humus;
(4) lime stabilization, the application of lime to sludge to raise the pH; and (5) air drying,
exposure of a layer of sludge to air to drain or dry. To varying degrees, these processes will
also lower the densities of pathogens in sludge. According to Feachem et al. (1983), "the
essential environmental factors in limiting pathogen persistence are time and temperature."
The success of a given treatment process in reducing the pathogenicity of an effluent or
sludge thus depends, in general, upon its retention time and the creation of an environment
especially hostile to particular organisms. U.S. EPA has established regulations in 40 CFR
257 for these processes to qualify as Processes to Significantly Reduce Pathogens (PSRP).
Additional processes, either singly or in combination with PSRPs, have been defined as
Processes to Further Reduce Pathogens (PFRPs).
3.2.1. Survival in Treatment Processes. Several studies have evaluated sludge treatment
processes according to the pathogen reductions that were achieved (Yanko, 1988; Ward et
al., 1984; Pedersen, 1981; U.S. EPA, 1988). Comparison of pathogen reductions among
processes is difficult because a single process will exhibit a wide range of removal
efficiencies.
Yanko (1988) examined the final sludge products from 24 treatment facilities every
other month for one year and ranked sludge treatment processes according to the densities
of indicator microorganisms (coliforms, fecal streptococci, plate count organisms, fungi and
bacteriophages). Processes that were most to least effective in reducing microorganism
densities were heat drying, aerated windrow composting, anaerobic digestion with air drying,
windrow composting, in-vessel composting, proprietary composting, thermal conditioning
(dewatered), aerobic digestion with air drying, and static pile composting. Yanko (1988)
cautions that the poor ranking of the static pile composting method should not be viewed
as an indictment of that method since a wide range of concentrations of microorganisms
existed in samples from these facilities. He also notes that the anaerobically digested air-
dried sludges compared favorably with the composted sludges in achieving lower
microorganism densities.
3-12
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In his review of density levels of pathogenic organisms in municipal wastewater
sludge, Pederseri (1981) concludes that lime stabilization can produce bacterial reductions
of >2 orders of magnitude, that anaerobic digestion averages reductions of 1-2 orders of
magnitude, and that mesophilic compostifig may be effective with proper temperature
maintenance. Data on aerobic digestion, although limited, indicated bacterial reductions
of <1 order of magnitude. A reduction of one order of magnitude is equivalent to one Iog10
reduction. Data were insufficient to evaluate bacterial reductions by air drying.
According to Ward etal. (1984) and U.S. EPA (1988), typical sludge treatment
processes (mesophilic anaerobic digestion, aerobic digestion, composting, air drying, and
lime stabilization) achieved reductions of 0.5->4 orders of magnitude. Lime stabilization
and composting gave reductions of 2->4 orders of magnitude. The degree of inactivation
during composting depends mainly on the temperature achieved in the process, higher
temperatures producing greater inactivation. Few data were available on bacterial
inactivation by aerobic digestion, but the laboratory and full-scale results examined indicate
reductions similar to, or greater than, those of mesophilic anaerobic digestion (0.5-4 orders
of magnitude). Thermophilic digestion (50°G) kills pathogenic bacteria and indicators. With
air drying, bacterial reductions depend on the time that sludge is retained at various
moisture levels. With 50% solids, reductions of 2-3 orders of magnitude per month were
observed.
Based on these reviews (Pedersen, 1981; Ward et al., 1984; U.S. EPA, 1988),
composting and lime stabilization processes appear to produce the best reductions of
bacterial densities in sludge. Ward et al. (1984) state that composting is the best method
since the high temperatures destroy all sludge pathogens.
3.2.2. Density of Bacteria in Treated Sludge. Table 3-5 lists bacterial densities in treated
sludge as presented by U.S. EPA (1988), Pedersen (1981), Yanko (1988) and others. There
are few data in the literature on the densities of the pathogenic bacteria of concern in
treated sludge; most bacterial studies report on indicator groups or Salmonella. These
densities vary widely as a result of a number of factors: the variation in concentrations of
organisms in raw wastes, the nature of the sludge treatment process used, and the lack of
standardization and sensitivity of detection methods. ,-
3-13
-------
TABLE 3-5
Bacterial Densities in Treated Sludge
Bacteria
Salmonella
Treatment
Process
High Rate
Anaerobic
Digestion
High Rate
Anaerobic
Digestion
High Rate
Anaerobic
Digestion
High Rate
Anaerobic
Digestion
High Rate
Anaerobic
Digestion
Aerobic Digestion
Forced Air
Composting (toe)
Forced Air
Composting
(Ccnter-40 cm)
Forced Air
Composting (toe,
center, bottom)
Deep-Pile Bin
Composting
Deep-Pile Bin
Composting
Deep-Pile Bin
Composting
Deep-Pile Bin
Composting
Lime Stabilized
(2-4.4% solids)
Density
Geometric Mean
MPN/100 mL or
number/100 mL
<3
6.2 x 10'
range of means 6 x
104-7 x 10s
(range 2 x 104-1.0 x
10')
1.6 x 10°
4.8 x 10' (range 3 x
10°-1.1 x 10"
<3/100 mL wet
sludge
1.1-<10 MPN/gdwt
<0.3-<8 MPN/g d wt
<10 MPN/ g d wt
>0.1 MPN/g d wt
0.2 MPN/g d wt
4.9 x 10' MPN/g d
wt
<0.4-38 MPN/g d wt
Range of means ND-
23 (range ND-23)
Detention
Time
20 d
21 d
9-60 d
14-15 d
20 d
3.6-11 d
28 d
28 d
25d
17 d compost
14 d storage
24 d
9d
9d
Ih
Conditions
60°C
35"C
34-37°C
35°C
36°C
40-65°C
15-70-C
15-60°C
24-48°C
36-43°C
34°C avg.,
58°C max.
36-58°C
43-59°C
pH 11.6-12.4
Reference
Stern and Farrell,
1977
Stern and Farrell,
1977
Cooke et al., 1978
Lue-Hing et al.,
1977
Jewell et al., 1980
Kabrick et al.,'
1979
Epstein et al.,
1976
Epstein et al.,
1976
lacoboni and
LeBrun, 1978
lacoboni and
Livingston, 1977
lacoboni and
LeBrun, 1978
lacoboni, 1977
lacoboni and
LeBrun, 1978
Counts and
Shuckrow, 1974
3-14
-------
TABLE 3-5 (continued)
Bacteria
Salmonella (cont.)
- •
Yersinia
Yersinia
emerocolitica
Treatment
Process
Lime Stabilized
(2- 4.4% solids)
Lime Stabilized
Lime Stabilized
Static Pile
Composting
Windrow
Composting
High Rate
Anaerobic
Digestion
Standard
Anaerobic
Digestion
Anaerobic
Digestion
(Primary Sludge)
Anaerobic
Digestion (Waste
Activated Sludge)
Lime Treated— pH
11.5 (Primary
Sludge)
Static Pile
Composting
Windrow
Composting
Anaerobically
Digested
High Rate
Anaerobic
Digestion
Lagooned after
Anaerobic
Digestion
Density
Geometric Mean
MPN/100 mL or
number/100 mL
Range of means 6-
105 (range 6-170)
< 1-1.3
<0.6
Range of means
1-44 MPN/g (range
< 0.1-85,000)
Range of means
< 0.2-10 MPN/g
(range <0.1-16,000)
>2.4-<24 CFU/g
>2.0 CFU/g
29/100 mL
7.3/100 mL
< 3.0/100 mL
Range of means 0.6-
6 MPN/g (range
< 0.1-2,500,000)
<0.2 MPN/g (range
<0.1-3.3)
106-109/g wet wt
ND
ND
Detention
Time
24h
1.4-4.1 h
21 d composting,
30 d curing
40-90 d
20-30 d
21 d composting,
30 d curing
40-90 d
lid
Conditions
pH 11.6-12.4
pH 12.0-12.4
pHll.5
Reference
Counts and
Shuckrow, 1974
Noland et al.,
1978
Parrel! et al., 1974
Yanko, 1988
Yanko, 1988
Dudley et al.,
1980
Dudley et al.,
1980
Kenner et al.,
1971
Kenner et al.,
1971
Kenner et al.,
1971
Yanko, 1988
Yanko, 1988
Metro, 1983b
Dudley et al.,
1980
Dudley et al.,
1980
3-15
-------
TABLE 3-5 (continued)
Bacteria
Yayinla
cntaocolitita
(cont.)
Catnpytobacter
Escherichia coti
SKgflla
-• Treatment
Process
Standard
Anaerobic
Digestion
Windrow-
Composting
Static Pile
Composting
NG
Static Pile
Composting
Windrow
Composting
High Rate
Anaerobic
Digestion
(lab scale)
Aerobically
Stabilized
High Rate
Anaerobic
Digestion
Lagooned after
Anaerobic
Digestion
Standard
Anaerobic
Digestion
Anaerobic
Digestion
(Primary Sludge)
Anaerobic
Digestion (Waste
Activated Sludge)
Lime Treated-pH
11.5 (Primary
Sludge)
NG
Density
Geometric Mean
MPN/100 mL or
number/100 mL
2.0X105 CFU/g ,
ND
ND
ND
Range 890-10,000
MPN/g (estimate)
Range 0.05-10,000
MPN/g (estimate)
Ixl06-8xl06/100 mL
2xlOs/100 mL (range
lOMO7)
1.7xl06 CFTJ/g
8.8xl06 CFU/g
2.0X105 CFU/g
0.39/100 mL
0.32/100 mL
0.014/100 mL
ND
Detention
; Time
20-30 d
40-90 d
21 d composting,
30 day curing
21 d composting,
"30 day curing
40-90 d
6-20 d
lid
20-30 d
Conditions
. '
33°-40°C
Reference
Dudley et al.,
1980
Yanko, 1988
Yanko, 1988
Ottolenghi and
Hamparian, 1987
Yanko, 1988
Yanko, 1988
McKinney et al.,
1958
Hess and Breer,
1975
Dudley et al.,
1980
Dudley et al.,
1980
Dudley et al.,
1980
Kenner et al.,
1971
Kenner et al.,
1971
Kenner et al.,
1971
Ottolenghi and
Hamparian, 1987
3-16
-------
TABLE 3-5 (continued)
Bacteria
Shigella (cont.)
Listeria
monocytogenes
Treatment
Process
High Rate
Anaerobic
Digestion
Lagooned after
Anaerobic
Digestion
Standard
Anaerobic
Digestion
NO
Air Drying after
Anaerobic
Digestion
Density
Geometric Mean
MPN/100 mL or
number/100 mL
ND
ND
>20 CFU/g
800->18,000/L
53/g sludge cake
Detention
Time
11 d
20-30 d
30 d digestion, 5
wk drying
Conditions
Reference
Dudley et a!.,
1980
Dudley et al.,
1980
Dudley et al.,
1980
Watkins and
Sleath, 1981,
Al-Ghazali and
Al-Azawi, 1988b
Sources: U.S. EPA, 1988; Pedersen, 1981; Yanko, 1988; Rao et al., 1986
ND = Not detected
NO = Not given
3-17
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The concentrations of pathogens in raw municipal wastes vary greatly with region,
season, the nature of the population and its size. Salmonella spp. were found at
concentrations of K^-IO3 organisms/g dry weight in primary sludge and PxlO2 organisms/g
dry wt in secondary sludge (Ward et al., 1984). Shigella spp. were found at concentrations
of "lO1 organisms/g dry weight in untreated wastewater sludges (Harding et al., 1980).
Based on his review data, Pedersen (1981) calculated average (geometric mean) Salmonella
densities in primary, secondary, and mixed sludges of 4.1X102 organisms/g dry wt, S.SxlO2
organisms/g dry wt, and 2.9X102 organisms/g dry wt, respectively. The densities of
Salmonella in primary, secondary, and mixed sludges appear to be quite similar. Sagik et
al. (1979) estimated densities of Leptospira and Yersinia in raw sewage sludge of 4.6X103
CFU/100 mL and 5.8xl08 CFU/100 mL, respectively. Shigella levels in mixed sludge were
reported below detection levels (LaConde et al., 1978).
Concentrations of pathogenic bacteria are affected by sludge treatment processes
(Section 3.2.1.). Yanko (1988) notes that densities in sludge products varied greatly between
sludge treatment facilities and between samples of products from the same facility.
Temperature, detention time, moisture, solids content, pH and interactions of these factors
may affect the concentrations of bacteria in the products. As noted in Section 3.2.1,
composting and lime stabilization appear' to give the best bacterial reductions based on
current data (Ward et al., 1984; U.S. EPA, 1988).
Salmonella densities in treated sludge products, as noted in Table 3-5, exhibit a wide
range of values. Yanko (1988) reported a range of <0.1-85,000 at a static pile composting
facility with a maximum mean Most Probable Number of 44 MPN/g as contrasted with a
range of <0.1-16,000 with a mean of 10 MPN/g at a windrow composting facility. Although
the occurrence of Salmonellae was higher than expected, the author considers the overall
mean concentrations from both facilities to be relatively low since few samples actually
contained high numbers of organisms. He concludes that there is no overt health hazard
considering the estimated high infective doses (Kowal, 1985), but use of these products in
home gardens may increase exposure risk, especially for young children, the elderly, or
immunologically compromised individuals.
3-18
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Yanko (1988) also gathered bimonthly data for a year on the presence of
microorganisms in final sludge products from 24 facilities representing many different
treatment processes. Of the 144 samples collected, detectable levels of Salmonellae
occurred in roughly 20% from 13/24 facilities. Salmonellae were isolated from >50% of the
samples at four sites. Two of these were drying bed plants, one was a thermal conditioning
process and one was a static pile composting operation. Salmonella densities ranged from
0.1-370,000 MPN/g.
Metro (1983b) reported high levels (106-109 organisms/g wet wt) of Y. enterocolitica
in anaerobically digested sludges. Dudley et al. (1980) reported 2x10* CFU/g total
suspended solids Y. enterocolitica in one digested sludge sample.
Yanko (1988) found that samples from the windrow composting facility were
infrequently positive for Yersinia (Y. enterocolitica and closely related species), and levels
were low. However, high concentrations of the organism were found in many samples from
the static pile composting facility. Yersinia populations were high during the winter and
spring months and were not detectable in summer and early fall. Based on a small number
of tests, the Yersinia appeared to be avirulent. These results are consistent with the
conclusions of Langeland (1983), who suggested that Yersinia may grow in sewage sludge,
since it was more frequently isolated in stored sludge. He isolated Yersinia species from
51% of 35 samples of stored sewage sludge, but serogroups commonly associated with
disease were not detected in the isolates. Yanko (1988) concludes that the relatively small
number of tests in his experiments.does not definitively establish that virulent yersiniae were
not present or that sludge does not serve as a reservoir for pathogenic Yersiniae.
Little is known about the occurrence and fate of enteropathogenic Escherichia coli
in sludge or the potential for its regrowth. Yanko (1988) found that toxigenic E. coli
occurred in very low levels, when present, in the final sludge products from the facilities in
this U.S. EPA study. However, on reexamining his data, he judged that the original
procedure was inadequate. Estimation of mean E. coli levels by other methods suggested
that these levels should have been higher. Kenner et al. (1971) report a range of 0.014-0.39
E. coff/100 mL of treated sludges; and Dudley et al. (1980) report 2.0xlOs-8.8xl06 CFU/g.
3-19
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According to Yanko (1988), there have been no published reports on the detection
of indigenous Campylobacter in sludge, and none were found in any of the sludge products
examined by Yanko (1988). However, he found the methodology for detecting the
organisms to be inadequate. Ottolenghi and Hamparian (1987) were able to detect seeded
Campylobacter at somewhat lower levels but isolated no indigenous Campylobacter in 99
sludge samples. Yanko (1988) concludes that the sensitivity of Campylobacter to oxygen and
its susceptibility to drying (Doyle and Roman, 1982) make it unlikely to persist through any
composting or sludge drying process.
Wolf et al. (1979) reported that samples of wastewater effluents used fof spray
irrigation of pastures were positive for Leptospira organisms. Yanko (1988) did not test for
Vibrio cholerae, Leptospira spp., or Shigella spp. Although responsible for a substantial
amount of disease, these organisms either have not been demonstrated in sludges
(Ottolenghi and Hamparian, 1987; Dudley et al., 1980) or sludge applied to land was not
considered an agent of transmission (WHO, 1981).
Watkins and Sleath (1981) found that Listeria monocytogenes could be isolated from
sewage, river water and sewage sludge in considerable numbers, often at counts higher than
Salmonella counts for those media. In Iraq, Al-Ghazali and Al-Azawi4(1988a) observed 89-
99.7% reduction in Listeria counts after anaerobic digestion of sludge for <30 days during
the cold months, but the remaining bacteria persisted during the summer in the sludge cake
resulting from air drying of this sludge. Monitoring the survival of L. monocytogenes in a
heap of sludge cake stored on farm land, Al-Ghazali and Al-Azawi (1988b) detected the
bacteria for 5 weeks on the pile surface and for 7 weeks in the interior, despite temperatures
of 56°C during this period. Moisture content was an important factor in the die-off of L.
monocytogenes in the sludge cake. They conclude that in a climate such as that of Iraq, 8
weeks of storage after sewage treatment can achieve disinfection by drying, with higher
temperatures accelerating the dewatering or drying process.
The comparison and interpretation of data that enumerate bacteria in sludges, soils,
and water are complicated by the lack of standard methods. Standard enumeration methods
exist for only a few pathogenic microorganisms. Because of procedural differences and staff
3-20
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experience, results will vary between laboratories Busing the same standard method.
Alteration in procedures and changes in staff within a laboratory create inconsistencies in
results from that laboratory. .: *r
In addition to the variations due. to methods and procedures, .sensitivity of a
particular method and steps to improve detection limits will affect results. The "most
probable number" (MPN) technique, which is a standard: for use with total and fecal
coliforms and Salmonella, is an indirect method, producing only approximate results.
More recent studies utilizing different methods appear to? offer more accurate
quantitation of.bacteria (Rollins and Colwell, 1986; Boardman et al, 1989; Desmonts et al.,
1990; Barcina et al., 1990). Boardman et al. (1989) reviewed,recent/studies on the
occurrence, persistence and detection of waterborne pathogens, emphasizing methods for
detecting, identifying and enumerating pathogenic bacteria, Desmpntset al. (1990)
recommend the indirect fluorescent antibody (IFA) technique for:detection of bacteria in
water and wastewater, noting that the IFA method detected Salmonella spp. in all of 12 raw
and chlorinated wastewater samples. In contrast,,enumeration by culture using the MPN
technique on these samples detected Salmonella in only 4/8 raw-water samples and 1/4
chlorinated samples. Rollins and Colwell (1986) observed significant differences between
results of direct count methods and those from spread plate cultures. Barcina etal. (1990)
also emphasized the importance of detection methods, asserting that CPU counts do not
give accurate quantitation of bacteria in aquatic systems because of factors that induce
inability to form colonies, such as visible light. Instead,- under light-induced stress^ the
bacteria form somnicells, i.e., viable but nonculturable cells. Desmonts; et al. (1990) are
studying the health significance of such nonculturable organisms. Rollins and Colwell (1986)
are also concerned about the epidemiologic implications of nonculturable Campylobacter
spp., and they urge re-evaluating methods used to monitor or detect the bacteria in
environmental media. , - : ;:: v. :
Although properly disinfected sludges should be virtually free of pathogenic bacteria,
inadequate or Incomplete treatment or recontamination may result in a population of
organisms that has the potential for regrowth given the proper environmental conditions.
According to Ward et al. (1984), bacteria associated with gastroenteritis are the only
.... 3-21
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bacterial pathogens likely to regrow, and bacteria of the genus Salmonella are capable of
significant regrowth in sludges. Al-Ghazali and Al-Azawi (1988a) cited examples of
multiplication of Listeria monocytogenes in soil and on grass clippings and emphasized the
possibility for multiplication in sludge following sewage treatment, especially if sewage
sludge cake were insufficiently dewatered. The regrowth capacity of various species and
strains of bacteria is not known but is likely to vary among strains and to depend on
conditions such as moisture, organic content, pH, sunlight, temperature and microbial
antagonism.
A number of studies have indicated that a 20% moisture content is a requirement
for growth of enteric bacteria in sludge (Ward et al., 1984). In dried sludges at less than
10% moisture, bacteria do not multiply but will survive. Therefore, a moisture content
between 10 and 20% will allow die-off while preventing regrowth. Regrowth of bacterial
pathogens is possible as pH decreases following lime stabilization if the sludge has not been
sufficiently limed. Russ and Yanko (1981) determined that a carbon/nitrogen ratio of £15
is necessary for Salmonella regrowth in composted sludge. Russ and Yanko (1981) and
Yeager and Ward (1981) discuss regrowth in relation to the various chemical and physical
characteristics of sludges.
Hussong et al. (1985) found that 12% of 30 municipal sewage sludges were positive
for Salmonellae. A high value of 17,000 MPN/g was suspect since Salmonellae were not
detected in raw sludge from this sample site. However, a value of 21 MPN/g was
determined from another sample. When these composts were seeded with Salmonellae, the
Salmonellae death rate over 24 hours at 36°C averaged 0.15/hr. However, in sterile
composts seeded with Salmonellae, the populations grew. Both growth and death rates were
associated with moisture levels. Low pH (<5.0) compost prevented regrowth. The authors
conclude that competitive influences suppressed Salmonellae in the nonsterile compost, and
suggest that methods to sterilize compost may actually encourage regrowth through removal
of the safety factor provided by the resident microflora.
When seven D&M municipal sewage sludges from two composting facilities were
examined for the occurrence of microorganisms, some products frequently contained
bacterial pathogens, and some products contained high densities of bacteria (Yanko, 1988).
3-22
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Few Salmonellae were detected in the finished compost from the windrow composting
facility; however, the bagged product containing mixtures of compost and amendments
frequently contained Salmonellae and often at relatively high concentrations.
The results of the samples from the windrow facility clearly suggest regrowth of
Salmonellae (Yanko, 1988). The population increase was concurrent with increases in the
indicator bacteria and fungal populations; This appears inconsistent with the conclusions of
Hussong et al. (1985) that the active indigenous flora of compost are antagonistic to the
regrowth of Salmonellae. In their experiments, regrowth of 5. typhimurium and S. newport
occurred when they were inoculated into sterile compost, but die-off occurred when they
were inoculated into untreated compost. Russ and Yanko (1981) demonstrated initial
regrowth of indigenous Salmonellae in compost in the presence of competing microflora,
with subsequent die-off. Yanko (1988) suggests that the inconsistency between his data and
those of Hussong et al. (1985) may be explained by differences in the species studied or by
differences in experimental design.
Millner et al. (1987) found that suppression of growth and death of Salmonellae in
composted sewage sludge were mediated by bacteria and actinomycetes. Suppression of
Salmonellae by compost microbes occurred with.prior occupation of the substrate by the
microorganisms; the types and relative amounts of microbes present at the time Salmonellae
were introduced affected the relative amounts of Salmonellae regrowth and death. Prior
colonization of compost by coliforms accounted for 75% of Salmonellae death, and prior
colonization of compost by other gram-negative bacteria resulted in compost that inhibited
the growth of Salmonellae. The authors conclude that regrowth should not be a problem
in properly cured compost. However, treatment processes that eliminate curing or allow
reheating to thermophilic ranges will reduce antagonistic organisms and provide an
environment that encourages regrowth of Salmonellae. ', '
Ward et al. (1984) suggest that management of sludge after treatment could prevent
or minimize regrowth. Among the management practices recommended are: sludge
handling methods that minimize recontamination, a treatment chain to produce conditions
that limit regrowth, and addition of soil to disinfected sludge.
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Yanko (1988) concludes that bacterial^ monitoring would be valuable for sludge
products that are to be used for home gardens or other public areas since bacterial
pathogens can be found in D&M sludge in highly variable densities. The presence of these
pathogens, particularly Salmonellae and possibly Listeria (Al-Ghazali and Al-Azawi,
1988a,b), poses a potential health hazard.
3.3. VIABILITY AND SUKVTVABILIIY OF BACTERIA
Since bacterial survival in soil, water and air is so dependent on environmental,
physical, chemical and biologic factors, establishing definitive survival or die-off rates is
practically impossible now. However, the data indicate that bacteria generally survive longer
in underlying sediments than in water, and survival is shortest in aerosols.
33.1. Survival in Soil. Microorganisms are inactivated in soil at rates that vary with the
type of organism and its condition; the application method; the degree of predation and
competition by other microorganisms; atmospheric conditions; and the physical and chemical
composition of the soil (Moore et al., 1988; Gerba et al., 1975; Gerba and Bitton, 1984;
Kowal, 1985). As a result of the large number of factors that affect survival in soil, there
is a wide range of recorded survival times for bacteria. Due to deficiencies in detection
methods, final data are more often qualitative than quantitative. Table 3-6 summarizes data
on die-off of bacteria in soil. Unlike viruses and protozoa, bacteria do not necessarily
decrease in numbers in soil; given suitable environmental conditions, nutrients, and minimal
competition from other microorganisms, bacteria may multiply (Feachem et al., 1983).
Because bacterial survival time in soils is strain specific, the survival of individual
pathogenic strains must be determined. Matthess and Pekdeger (1985) conclude that since
it is not yet possible to predict accurately the elimination constant based on the relevant
physical, chemical and biologic parameters, strain- and environment-specific survival rates
(elimination constants) must be measured. Since environmental conditions are so variable,
there is a need to develop correlations of die-off rates with one or more of the most
significant environmental parameters, e.g., temperature and humidity.
The results reported by Jones et al. (1983) indicate ranges of 9-25 days and 19-45
days for the Tgo (time in days to achieve a 90% reduction in numbers) and Tw (time in days
3-24
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TABLE 3-6
Bacterial Die-Off in Soil
Bacteria
Salmonella
Die-Off Rate
in days
T T
*90 199
(soil depth)
16 33
(5-15 cm)
14 29
(5-15 cm)
22 45
(5-15 cm)
. 4 7
(25-30 cm)
17 30
(25-30 cm)
11 24
(0-5 cm)
23 39
(0-5 cm)
61
(0-5 cm)
25 45
(0-5 cm)
9 19
(0-5 cm)
11 21
(0-5 cm)
6
(5-15 cm)
22
(5- 15 cm)
Die-Off
Rate
Constant
(Iog10 day ')
0.062
0.070
0.045
0.268
0.063
0.087
0.047
0.016
0.042
0.108
0.089
0.167
0.045
Sludge
Treatment/
Application
Anaerobic,
dig.; sprayed,
tilled
Anaerobic,
dig.; sprayed,
tilled
Anaerobic,
dig.; sprayed,
tilled
Raw, injected
Raw, injected
Sprayed
Spread
Spread
Raw, spread
Raw, spread
Digested,
spread
Injected
Injected
Soil
Conditions
Sand, pH
4.8; 9°C
Sand, pH
5.9; 9°C
Rich loam
Light sand,
pH 8.0;
15°C
Light sand,
pH 8.0;
9°C
15°C
4°C
15°C
14°C
Reference
Watson, 1980
Watson, 1980
Watson, 1980
Andrews et al.,
1983
Andrews et al.,
1983 '
Watkins and
Sleath, 1981
Jones et al.,
1983
Jones et al.,
1983
Jones et al.,
1983
Jones et al.,
1983
Jones et al.,
1983
Jones et al.,
1983
Jones et al.,
.1983
3-25
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TABLE 3-6 (continued)
Bacteria
Salmonella (cont.)
S. typhimurium
Die-Off Rate
in days
TM Tw
(soil depth)
15
(0-5 cm)
12 22
(0-5 cm)
11 22
(5-15 cm)
8 18
(5-15 cm)
11 22
(5-15 cm)
3 7
(5-15 cm)
14 28
' (25cm)
10 18
(0-5 cm)
6 11
(0-5 cm)
11.2-22.2
OW
Die-Off
Rate
Constant
(Iog10 day'1)
0.067
0.087
0.091
0.118
0.091
0.292
0.575 avg
(mm 0.091;
max 2.99)
0.071
0.113
0.183
0.0155
0.045-0.089
Sludge
Treatment/
Application
Raw,
activated;
spread
Raw,
activated;
spread
Raw; mixed
and buried
Raw; mixed
and buried
Raw; mixed
and buried
Raw; mixed
and buried
Raw; mixed
Seeded
Seeded
Seeded
Inoculated
Inoculated
Soil
Conditions
-1°C
1°C
Heavy clay,
12°C (soil)
Medium
clay loam,
12°C (soil)
Light sand,
12°C (soil)
Light sand,
15°C
Soil-water-
plant system
Survival
chambers in
situ in
cypress
strand
70-85% dry
matter, grey
loam
Reference
Kenner et al.,
1971
Kenner et al.,
1971
Dickson and
Tribe, 1986
Dickson and
Tribe, 1986
Dickson and
Tribe, 1986
Dickson and
Tribe, 1986
Reddy et al.,
1981
Bergstrom and
Langeland,
1981
Larkin et al.,
1978
Larkin et al.,
1978
Scheuerman et
al., 1986
Chandler and
Craven, 1980b
3-26
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TABLE 3-6 (continued)
Bacteria
5. typhimurium
(cont.)
S. enteritidis
S. adelaide
Escherichia coli
Die-Off Rate
in days
^90 *»
(soil depth)
9.7-29.4 CT,,)
3.2-6.8 (TM)
10.3-24.3
(T*,)
4.6-10.2 (T%)
2.0-6.0 (T*,)
4.5-13.0 (Tw)
31.5-46.5
(T*,)
12.5-17 (T,o)
16.5-18.5
OW
7-16.5 (TM)
2.5-24.3
(T*,)
Die-Off
Rate
Constant
(loglo day1)
0.034-0.103
0.147-0.312
0.0412-0.097
0.0975-0.218
0.167-0.50
0.0215-
0.0315
0.0583-0.080
0.0540-
0.0606
0.0606-
0.143
0.0408-0.40
Sludge
Treatment/
Application
Inoculated
Inoculated
Inoculated
Inoculated
Inoculated
Inoculated
Soil
Conditions
70-85% dry
matter,
reddish
brown silty
loam
70-92% dry
matter,
black sand
70-85% dry
matter, grey
crackling
loam
70-85% dry
matter, red
brown clay
loam
Fine sand
Unsaturated
soil (96.9%
coarse
sand)
Unsaturated
soil (76.9%
coarse
sand)
Saturated
soil (96.9%
coarse
sand)
Saturated
soil (76.9%
coarse
sand)
70-90% dry
soil (pot)
Reference
Chandler and
Craven, 1980b
Chandler and
Craven, 1980b
Chandler and
Craven, 1980b
Chandler and
Craven, 1980b
Dazzo et al.,
1973
Parker and
Mee, 1982
Parker and
Mee, 1982
Parker and
Mee, 1982
Parker and
Mee, 1982
Chandler and
Craven, 1980a
3-27
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TABLE 3-6 (continued)
Bacteria
E. coli (cont.)
E. coli B
Shigella
Die-Off Rate
in days
*90 *99
(soil depth)
Die-Off
Rate
Constant
(Iog10 day1)
0.92 avg
(min 0.15;
max 6.39)
0.0168
0.294 avg
(min 0.268;
max 0.320)
Sludge
Treatment/
Application
Inoculated
Soil
Conditions
Soil-water-
plant system
Survival
chambers in
situ in
cypress
strand
Soil-water-
plant system
Reference
Reddy et al.,
1981
Scheuerman et
al., 1986
Reddy et al.,
1981
'Source: Sorber and Moore, 1987
3-28
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to achieve a 99% reduction in numbers) values, respectively, for Salmonellae survival on the
soil surface (Table 3-6). This excludes their report of a 61-day Tgo for a low ambient
temperature. The values reported by Watkins and Sleath (1981) fall within these ranges,
and the results of Watson (1980), Andrews et al. (1983) and Dickson and Tribe (1986)
for deeper soil suggest similar values.
In their review, Sorber and Moore (1987) note that the exceptionally long survival
times recorded in the literature resulted from seeding sludge with high concentrations of
Salmonellae. Normally indigenous Salmonellae survive less than 2 months after application,
but low levels have been found as long as 3-5 months later.
Comparing the survival of Listeria monocytogenes with that of Salmonella in sewage
sludge applied to land, Watkins and Sleath (1981) observed that Salmonella died off rapidly
but that the numbers of L. 'monocytogenes persisted relatively unchanged over the 8-week
period of the study. They suggest that the numbers of L. monocytogenes added to the
environment from sewage and sewage sludge may be greater than the numbers of
Salmonellae contributed by these sources. According to Al-Ghazali and Al-Azawi (1988a),
L. monocytogenes may be overlooked or not recognized because of its resemblance to the
enterococci and difficulties in isolating it from heavily contaminated material. Al-Ghazali
and Al-Azawi (1988b) observed that L. monocytogenes in sludge cake was heat tolerant
during air-drying, surviving temperatures of 56°C, but was inactivated at low moisture levels.
Since the concentrations of bacterial pathogens in treated sludge are often very low,
most studies have measured concentrations of groups of bacteria as indicators for pathogenic
organisms. In their review, Sorber and Moore (1987), using fecal coliforms as an indicator
for E. coli, report that 90% of fecal coliforms could not be recovered within 6 weeks of
sludge application. They report that, in general, survival of microorganisms increases with
decreasing temperatures; but at depths of 5-15 cm, fecal coliforms increased with increasing
temperatures. They attributed the increase, which was temporary, to the enriched
environment of the sludge-amended soil and the protection from stresses that depth
provided.
3-29
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In France, Berron et al. (1981) observed that low levels of Salmonellae were present
in the overlying sludge crust on the soil for 1 month following application of thickened
sludge. Low levels were found in the soil itself for 15 days. Koser (1967) examined the
grass, topsoil (1 cm), and sludge crust of German pastures spread with raw sludge in spring.
Salmonellae were found in the crust for the 16-week monitoring period, in the topsoil for
10 weeks, and on the grass for 5 weeks. Yanko et al. (1978) found that digested anaerobic
sludge spread and disked into soil gave positive Salmonellae sample results up to 4-5 months
later. •
In a Canadian study, when raw sewage sludge was injected into soil at a depth of 15
cm, Salmonellae were recovered up to 3 months later, but final samples taken 1 year post
injection were negative (Carroll and Ross, 1983; Sekla and Stackiw, 1983). When
anaerobically digested sludges were injected at 15 cm, no viable Salmonellae were detected
at any time. Wallis et al. (1984) found that E. coli persisted longer on the soil surface of
a sludged grass plot than on the soil surface of an injected field. E. coli populations
remained on the soil 154 days after sludge application to the grass plot but disappeared after
66 days on the injected field.
The presence and activity of other microorganisms, such as bacteria and protozoa,
in the subsurface soil and groundwater affect survival of pathogenic bacteria in soil, possibly
by competition for nutrients (Yates and Yates, 1988). Survival of enteric pathogens
increases in sterilized soil since they are not antagonized or suppressed by soil microflora
(Gerbaetal., 1975).
Enteric bacteria are extremely sensitive to drying, and sunlight has been shown to be
bactericidal (Gerba and Bitton, 1984). In the long-term study done by Strauch et al. (1981)
in Germany, anaerobically digested sludge inoculated with Salmonella senftenberg at a
concentration of 107 organisms/mL was spread on various soil types in different forest
stands. When spread in summer, S. senftenbergwas found in the sludge crust for a range
of 424-640 days; when spread in winter, S. senftenberg persisted in this crust for 104-350 days.
There was no clear relationship between soil type and survival; but longer survival appeared
to be associated with heavily wooded areas, perhaps because of reduced sun. The authors
3-30
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suggest that the longer survival with summer-spread sludge might be due to the organism's
growth and acclimation at favorable summer temperatures before the onset of unfavorable
winter conditions.
Jones et al. (1983) reported on several studies in the United Kingdom where the
survival of Salmonellae was evaluated on the. grass of sludge-sprayed pasture land. Colder
temperatures appeared to favor longer survival since the T^, 61 days, was higher in winter
(mean temperature 4°G) than the Tpo values of 23 and 25 days determined in fall (mean
temperature 15°). However, they report on another study where subsurface injections of
sludge in late summer and late winter gave T^ values of 6 and 22 days, respectively, despite
similar mean temperatures. Activity of soil microilora and attributes of the sludge itself
were offered in explanation.
Several studies have reported that bacteria survive longer in moist soils than in dry
soils. Some investigators conclude that soil moisture is the dominant factor for survival of
enteric bacteria in soil (Yates and Yates, 1988). Soils with a higher percentage of organic
matter have a greater water-holding capacity and are more conducive to pathogen survival
than sandy soils, in which drying is rapid because of their low moisture-retaining capability
(Kowal, 1985). In addition, the presence of organic matter in soils can provide conditions
conducive to regrowth, demonstrated by the regrowth of S. typhimurium and£. coffin buried
feces (Temple et al., 1980). ..
Enteric bacteria survive longer in alkaline soils than in acid soils of pH.3-5. While
low pH may affect the availability of nutrients, the activity of antimicrobial agents, and the
ability of bacteria to survive, some bacteria are also susceptible to inactivation at highly
alkaline pH values. Bacterial strains appear to have optimum pH tolerance ranges that can
be influenced by other environmental conditions (Yates and Yates, 1988).
Hellstrom and Marshall (1978) found that Leptospira interrogans serovar pomona
could survive for 42 days in acidic (5.5) New Zealand soil with simulated winter field
conditions, even with low moisture. The authors suggest this may explain the prevalence of
leptospirosis in cattle on acid New Zealand soils.
Temperature has been observed to have an inverse relationship to bacterial survival
in soil and water, some bacteria surviving at freezing or subfreezing temperatures (Yates
• 3-31
-------
and Yates, 1988). When Andrews et al. (1983) injected raw sludge into a sandy soil, T^
and Tw values for the summer period were 4 and 7 days, respectively, while in winter they
were 17 and 30 days, respectively. The authors conclude that the longer survival might be
related to the lower soil temperature in winter. Kenner et al. (1971) observed similar Tw
and T^ values for Salmonellae in winter.
When liquid digested anaerobic and composted sludges were spread and disked into
fields at rates as high as 90 dry metric tons/ha, Yanko et al. (1978) found that Salmonellae
could be recovered from the soil for 4 (summer) to 5 (winter) months after application.
They also observed that recovery was greater in moist soils (150 MPN/g) than in dry soils
(10 MPN/g) at the end of the study.
Zibilske and Weaver (1978) observed an interaction between moisture and
temperature in the survival of 5. typhimurium inoculated into samples from two soil types
and held at 5°, 22°, and 39°C. Death was rapid in dry clay; no bacteria were detected at any
of the test temperatures after 3 days under dry soil conditions. At 39°C, death was also
rapid, but with moisture in the soil, some were present for at least 3 days. Dry soil
conditions and 39°C incubation temperatures were also the most detrimental treatment for
the organism in sandy loam. With moist conditions, some survived for 42 days at 39°C; and
at 22°C, some survived for 42 days in dry soil. In a few samples, Salmonellae populations
increased at 3 days but decreased afterwards. It is difficult to derive die-off rates from their
data because many of the inocula grew before die-off began.
Guy and Visser (1979) studied the adsorption and survival of E. coli in sterilized soil
slurries made from soils from two depths (0-10 cm and 10-20 cm) of six soil types in New
Zealand. Inoculated slurries were held at 10°C. E. coli was not greatly adsorbed in any of
the soils studied and survived 2-3 months, except in clay loam where survival was limited to
25 days. Results indicate that the combination of high clay content and low soil pH reduces
survival time of E. coli.
In the United Kingdom, when anaerobically digested sludges were sprayed onto sandy
and organic soils and tilled after 2 weeks, Watson (1980) observed that Salmonellae survived
for 6-7 weeks. Although the sludge was applied to the sandy soils at higher rates than to
the organic soil, die-off in the sandy soils was more rapid (averages of 15 and 30 days for
3-32
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Tpo and T^ values, respectively) than in the organic soil (22 and 45 days for T,*, and Tw,
respectively). The author concludes that desiccation in sandy soils may contribute to rapid
die-off.
In their review of survival and transport of pathogens in sludge-amended soil, Sorber
and Moore (1987) conclude that Salmonella has a T^ range of 3-61 days in soils at depths
of 0-30 cm. In many of the papers reviewed, Salmonella inactivation appeared to be more
rapid in summer than in winter. Temperature was the only physical or meteorologic
parameter related to microorganism survival in sludge-amended soil, survival increasing as
temperature decreased. In general, a 90% reduction of Salmonella occurs within 3 weeks
of sludge application. Gerba and Bitton (1984) conclude that 2-3 months should be
sufficient to reduce the levels of pathogenic bacteria in the soil to levels that are not
harmful.
3.3.2. Survival in Water. Much of the existing literature on bacterial survival in water
discusses duration of survival, but Table 3-7 summarizes a few studies yielding die-off rates
in aquatic systems. The survival of bacteria in groundwater depends primarily on the
biologic, physical and chemical conditions of the groundwater and on the processes that
control the transport of the bacteria (Matthess and Pekdeger, 1985), described in Section
3.4.3. In surveys of groundwater quality in the United States, 2-75% of groundwater samples
contained fecal coliforms (Gerba, 1985). The quality of groundwater has been related to
depth of the well. Shallow wells are more frequently positive for coliforms and average
higher coliform densities than deep wells. After periods of heavy rain, bacterial
contamination in wells may increase, the increase appearing first in the shallower wells.
Enteric bacteria show very little growth in groundwater (Matthess and Pekdeger,
1985). Pathogenic bacteria survive much longer in groundwater if there is little biologic
activity since under favorable environmental conditions the autochthonic (indigenous)
groundwater microorganisms can flourish, out-competing the pathogens and eliminating
them. Under oligotrophic conditions, enteric bacteria may survive or even increase slightly
before die-off begins to occur exponentially. Although there is a rapid elimination, the
bacteria may continue to exist in small numbers in groundwater for some time since the
half-life of most bacteria is in the range of 1-20 days.
3-33
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TABLE 3-7
Bacterial Die-Off in Aquatic Systems
System
Description
Well water inoculated
with pure cultures
(field, membrane filter)
(McFcters et a!., 1974)
Stream water
(membrane filter)
(McFcters and Stuart,
1972)
Inoculated river water
(lab study in flasks)
(Mitchell and Starzyk,
1975)
Storm water runoff (lab
study) (Geldreich et al.,
1968)
Storm water runoff (lab
study) (Geldreich and
Kcnner, 1969)
Organism
Enterococci
Shigella dysenteriae
Shigella sonnei
Shieella flexneri
Salmonella paratvohi A
Salmonella paratvohi B
Salmonella
typhimurium
Salmonella tvphi
Vibrio cholerae
Streptococci
Escherichia coli
(field study)
E. coli (lab study)
Escherichia coli
Salmonella
tvphimurium
Streptococcus faecalis
Salmonella
tvohimurium
Streptococcus faecalis
Salmonella
tvphimurium
Streptococcus faecalis
Streptococcus faecalis
Salmonella
typhimurium
Salmonella
typhimurium
pH
7.48
8.37
8;10
8.10
2.5
4.0
5.0
5.5
7.5
10.0
12.0
Not given
Not given
Not giyen
Season or
Temperature.
CO
10-12
4-6
5
10
15
20
25
10
0
5
10
20
0
5
10
20
0
5
10
20
Summer (20)
Winter (10)
.Summer (20)
Winter (10)
Length of
Study
4 days
5 days
20 days
14 days
14 days
Die-off Rate
(Iog10 days*')
0.328
0.322
0.295
0.270
0.452
3.01
0.452
1.21
1.00
0.370
0.851
1.356
0.065
0.100
0.213
0.428
0398
6.13
0.287
0.189
0.147
0.184
0.319
9.8
0.192
0.144
0.255
0.288
0.177
0.144
0.288
0.329
< 0.115
0.192
0.192
0.177
< 0.164
1.690
<0.164
0.307
< 0.164
0.324
1.354
3-34
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TABLE 3-7 (continued)
System
Description
Groundwater
(Bitton et al., 1983)
Continuous-flow stream
sediment microcosm
(lab study)
Burton et al., 1987
Lake water (lab study)
Awong et al., 1990 '
River Sowe water,
unfiltered (lab study)
Flint, 1987
River Coquet water,
filtered (lab study)
Evison, 1988
Organism
Escherichia coli
Fecal streptococci
Salmonella
tvohimurium
Pseudomonas
aerueinosa
Klebsiella oneumoniae
Escherichia coli
Salmonella newnort
Escherichia coli
Escherichia coli
Escherichia coli
Salmonella
typhimurium
Shieella sonnei
Yersinia enterocolitica
Campvlobacter fetus
pH
7.6
6.8-8.7
. ,.,.,..„...
Season or
Temperature
(°C)
22
16-21
15
25
30
4
15
25
37
2
5
10
15
20
25
2
5
10
15
20
25
2
5
10
15
20
25
5
10
5 .
10
15
. 20
25
Length of
Study
15 days
14 days
19 days
260 days
Dark
Die-off Rate
(loglo days-1)
0.158
0.029
0.130
0.16-0.205
0.18-0.44
0.21-034
0.39-0.70
0.05
0.20
0.19
0.411
0.497
0.696
1.44
0.0328
0.0466
0.0808
0.102
0.220
0.353
0.0255
0.0365
0.0325
0.102
0.137
0.129
0.422
0.243
0.174
0.0814
0.135
0.245
0.0382
:0.0228
0.156
0.204
0.286
0.400
0.890
•Source: Moore et al., 1988; Bitton et al., 1983
3-35
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Salmonella, Shigella, Escherichia coli, Vibrio cholerae, Streptococcus faecalis,
Campylobacter jejuni and Yersinia enterocolitica have recorded instances of occurrence and
survival in surface water, although evidence suggests significant variability in survival times
(Feachem et al., 1983). Generally, survival in surface water is decreased in natural waters
having resident competitive or predatory microorganisms relative to sterilized, filtered or
dead waters with little or no microbial life.
Chao et al. (1988) added IxlO7 CFU/mL of Yersinia enterocolitica to river water. The
total number declined 100-fold in 48 hours, but a lower concentration (IxlO5 CFU/mL)
decreased rapidly and were no longer recoverable by day 3. The authors conclude that
survival of Yersinia in river water was strongly influenced by predators and toxins in the
water. Groundwater, having lower levels of microorganisms, allowed longer survival of the
bacteria.
Flint (1987) found that E. coli could survive in autoclaved, filtered river water for
>260 days at temperatures of 4°-25°C, but survival decreased at 37°C. Possession of
antibiotic-resistant plasmids was not a factor in bacterial survival under starvation
conditions, but viral predators and bacterial competitors in the water were seen as the major
factors in disappearance of introduced bacteria in filtered (but not autoclaved) or untreated
water.
Jimenez et al. (1989) found that both E. coli and 5. typhimurium survived and
remained active in tropical surface waters for >5 days. Solar radiation reduces bacterial
survival in surface water as do high temperatures (Feachem et al., 1983). Barcina et al.
(1990) studied the effects of visible light on survival of E. coli and Enterococcus faecalis in
natural aquatic systems. Their studies indicated a stressful effect of illumination on these
bacteria, leading to a reduced ability to form colonies and an increase in the number of
somnicells (non-culturable cells). They discuss the limitations of direct count methods in
estimating numbers of enteric bacteria present in natural aquatic systems, and they suggest
the value of indirect activity measurements in evaluating the effects of fresh water and
seawater on enteric bacteria.
3-36
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By studying comparative survival of indicator organisms and pathogens in water,
Evison (1988) showed that a linear relationship exists for 1^ and light intensity, and for log
Tgo and temperature. The lethal effects of light increased with light intensity. Salmonella
typhimuriwn Type 12 survived longer than the other pathogens tested in light; E. coli and
Shigella sonnet survivals were lowest of those tested.
Dutka and Kwan (1980) studied bacterial die-off and stream transport, finding that
E. coli, Streptococcus faecalis and Salmonella thompson could survive for at least 28 days in
17°-18°C water. By examining the survival and transport rate of an indicator species
(Serratia marcescens), the authors showed that some members of the family
Enterobacteriaceae could survive from 8-22 days while being transported as much as 20 km
downstream.
Burton et al. (1987) developed a linear model to describe die-off rates in freshwater
sediments. Studying Pseudomonas aeruginosa, Salmonella newport, Escherichia coli, and
Klebsiella pneumoniae, they monitored bacterial survival in a variety of sediments for 14
days. Reductions ranged from 1-5 orders of magnitude in sediments, but survival rates in
the microcosm sediments were greater than rates in the overlying waters. Particle size was
the only sediment characteristic having an apparent relationship with bacterial survival: E.
coli and 5. newport survived longer in sediments containing at least 25% clay. The authors
review supporting evidence of higher densities of bacteria in freshwater sediments than in
the overlying surface waters. They suggest that at high initial concentrations (108 cells/mL)
bacteria could survive for months in sediments, much longer than in surface waters.
Resuspension of the upper-layer sediments, a reservoir in which pathogenic bacterial density
is greatest, can create a potential health hazard in cases of human ingestion in primary
contact waters.
3.3.3. Survival in Aerosols. Hickey and Reist (1975a,b) summarized the health significance
of airborne microorganisms from wastewater treatment processes, pointing out that land
application of wastewater by spraying or sprinkling could theoretically produce concentrated
viable aerosols. They report concentrations of viable particles in bacterial aerosols as high
as 41,200 viable particles/m3, the bacteria remaining viable and virulent far enough
3-37
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downwind to reach populated areas. They suggest, however, that correlation of levels of
airborne microorganisms with disease incidence has not been demonstrated, and they
recommend epidemiologic studies to evaluate whether there is a correlation.
Several reviews of health effects from bacteria in aerosols generated from wastewater
and sludge have been published (Kowal et al., 1981; Carnow et al., 1979; Sorber et al, 1984;
Camann et al., 1988; Bausum et al., 1983; Pahren, 1987; Burge and Marsh, 1978).
Sorber and Sagik (1980) reviewed the factors that affect the survivability of pathogens
in wastewater aerosols. There have been a number of studies to develop models for
dispersion and die-pff of microorganisms in aerosols (Camann, 1980; Bausum et al., 1983;
Teltsch et al., 1980; Cox, 1987). Table 3-8 summarizes bacterial die-off in aerosols.
When aerosols are generated, bacteria may suffer an initial rapid die-off, referred to
as aerosol shock or aerosol impact. Biederbeck (1979) suggests that rapid pressure changes
account for this phenomenon. Sorber and Sagik (1980) conclude that the combined effect
of relative humidity, temperature and sunlight account for aerosol shock. They note that
pathogenic bacteria are less affected by aerosol shock than indicator organisms. Wathes et
al. (1986) found that the initial die-off phase lasted "1 minute in E. coli.
Following this initial phase, bacterial decay of the aerosol may result from biologic
decay (die-off and loss of ability to form colonies) and physical decay (settling and
deposition) (Teltsch et al., 1980). Biologic decay is influenced by many factors, among
which are cellular physiology and environmental factors. Wathes et al. (1986) include the
composition of the culture and suspension fluid and the condition of growth and concen-
tration procedures in this list of factors.
Environmental factors that affect biologic decay include low humidity, high
temperatures, and solar radiation (Teltsch and Katzenelson, 1978; Kowal, 1985; Wathes et
al., 1986). Wathes et al. (1988) reported that Salmonella typhimurium viability in a dry
atmosphere (32% relative humidity) was 4% of its initial concentration whereas in high
humidity (72%) viability increased to 24.8%. Even though there was an initial sharp
reduction in viable bacteria, at 90 minutes after aerosol generation viability of the bacteria
was "1% of its initial value at all humidities. Airborne transmission of 5. typhimurium to
3-38
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TABLE 3-8
Bacterial Die-Off in Aerosols
Organism
(Reference)
Escherichia coli
Temperature
(°C)
22
22
22
40
17
26.5
24.5
25
25
26.5
25
25
Relative
Humidity
85%
10%
30-40%
30-40%
98%
50%
35%
55%
66%
42%
47%
66%
t1/2 (min)
390
55
37
14
9.33
6.57
4.68
2.86
1.86
0.81
Die-Off
Rate
(sec'1)
2.96xW5
2.1x10^
3.1x10"*
8.25X10-4
water:
9.4xlO"3
sewage:
S.lxlO'3
water:
6.4xlO'2
sewage:
6.6X10"2
water:
1.24xlO"3
1.76xlO*3
2.48X10"3
broth:
4.04x W3
6.22xHT3
1.42X10'2
Reference
Muller and
Dinter, 1986
Teltsch et al.,
1980
Katzenelson
et al., 1977
3-39
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calves induced infection in all groups exposed to the aerosol. Mice exposed to the same
aerosol showed some evidence of a dose response.
Marthi et al. (1990) found that survival of Pseudomonas syringae was greatest when
ambient temperatures were low (12°C) and humidity was high (70-80%). Bacterial cell
survival was reduced when droplet sizes were smaller and when cells were washed prior to
aerosolization. In evaluating the survival of four organisms sprayed in a greenhouse, Marthi
et al. (1990) found that viable counts of P. syringae, Enterobacter cloacae, and Klebsiella
planticola were reduced significantly while counts of Erwinia herbicola did not decline
significantly over the same 15-m distance. Although the authors present comparative
bacterial survival graphs, no quantitative die-off rates were generated. However, they do
mention evidence of quite rapid evaporation of droplets (10-30 seconds) at 22°C and 39%
relative humidity.
Teltsch and Katzenelson (1978) found a positive correlation between number of
aerosolized bacteria and relative humidity and a negative correlation between bacterial
concentration and solar radiation.. Approximately 10 times more bacteria were detected
during irrigation at night than in the daytime. Bacteria were detected when their initial
concentration in wastewater was >103. Median particle diameters were larger than 7/xm, but
"50% of bacteria sampled were associated with particles <7jiin. Teltsch and Katzenelson
(1978) observed that because the larger particles could be inhaled and subsequently
swallowed, they might prove to be a greater health hazard than the smaller particles, which
are considered to be a risk for respiratory tract infections because they penetrate the lower
respiratory tract.
Pahren (1987) reviewed studies (Johnson et al., 1980; Kenline, 1968) on microbial
survival in aerosols. In addition to die-off, diffusion in the atmosphere and deposition
diminish microorganism densities with time and distance (Kenline, 1968). Under average
conditions, percentage reduction in density would be 85% at 15 m, 91% at 30 m, and 98%
at 45 m. When Johnson et al. (1980) measured microbial aerosol densities near an activated
sludge plant, geometric mean counts of Proteus, Salmonella, and Shigella were all <2/m3.
Carnow et al. (1979) reported on the survival of bacteria in aerosols near an activated
sludge plant. Concentrations of viable particles (total aerobic bacteria-containing particles)
3-40
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were 198 particles/m3 at a distance of 0.8 km downwind and 155 particles/m3 at 1.6 km
downwind. They concluded that there were "no obvious adverse health effects on residents
potentially exposed to aerosol emissions" and no significant correlations between exposure
indices and infection rates or rates of self-reported disease. They caution, however, that
very few individuals were exposed to the highest pollution levels.
Bausum et al. (1983) estimated mean bacterial die-off in aerosols generated by a
sprinkler system using wastewater as 52% at 21-30 m downwind and 77% at 200 m
downwind. Levels of bacterial aerosols at 21-30 m averaged 485 CFU/m3. Of the particles
sampled at 21-30 m downwind, 66-78% were 1-5 Aim in diameter, the size range for
pulmonary deposition. The mean aerodynamic size of bacteria-carrying particles downwind
was 2.44-3.03>m as compared with the ambient (upwind) aerosol particle size of 4.15-4.59
Aim.
Since spray application of sludge produces even lower concentrations of bacteria in
aerosols than does wastewater spray, Sorber et al. (1984) conclude that bacterial aerosols
from sludge represent no serious health threat to individuals >100 m downwind of the
application site.
Little information is available on survival of pathogenic bacteria in particulates.
Mattsby and Rylander (1978) report a higher incidence of symptoms consistent with
endotoxin exposure (fevers and diarrhea) in workers exposed to wastewater dusts. Clifton-
Hadley and Enright (1984) found that Streptococcus suis type 2 survived in dust for <54 days
at 0°C and <25 days at 9°C but could not be isolated from dust at room temperature after
24 hours.
3.3.4. Survival in Agricultural Products. Jones (1983) reviewed Salmonella survival on
grass and suggested that the shorter survival time on grass than in soil might be related to
sunlight. He reported Salmonella survival times of 2-72 weeks on grass. Survival on grass
clippings was shorter than on uncut grass, and survival on grass near the soil surface was
greater than on the grass tips.
According to Taylor and Burrows (1971), when Salmonella dwMn-contaminated slurry
(107 organisms/mL) was applied to pasture, organisms persisted in the soil for up to 12
3-41
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weeks with varying climatic conditions. 5. dublin was recoverable from the grass up to the
6-inch level for 10-11 days (102-106 organisms/g) and from the 6-9 inch level for 4 days. The
persistence of the organisms appeared to be influenced by such environmental factors as
moisture and light, according to the authors. When E. co/f-contaminated slurry (106
organisms/mL) was applied to pasture, the organisms were recoverable in the soil for 8-10
days at between 102 and 104 organisms/g under varying climatic conditions described by the
authors as "warm," cloudy to sunny, and with "1 inch or more of rain during the course of
the experiments. From 103-105 organisms/g were recoverable for 4-7 days from grass at the
level of 0-1 inch and for 2-4 days from the level of 1-3 inches. The authors conclude that
little relationship was demonstrated between the prevailing weather conditions and the
survival of E. coli since no organisms were isolated after 10 days in the soil despite the
warm, moist conditions. Taylor and Burrows (1971) conclude that increased exposure to
sunlight and enhanced drying were the causes for reduced survival of bacteria when the
pastures were cut.
In his observations of alfalfa irrigated with municipal sewage lagoon effluent, Bell
(1976) concluded that sunlight, high temperatures, and low humidity destroy bacteria. For
a semiarid prairie region, he recommended that 2 sunny days elapse between irrigation and
grazing to effectively destroy Salmonellae- and enteropathogenic E. coli on alfalfa. Fecal
coliforms were killed in summer by 10 hours of daylight and in cold, wet weather by 28
hours of daylight.
Wallis et al. (1984) tested for the presence of primary (e.g., Salmonella) and
secondary (e.g., E. coli) bacteria in combined soil and vegetation samples from grassland to
which sludge was applied, a field in which sludge was injected, a control hayfield, and an
unsludged pasture. There were no Salmonellae in the initial sludge, and none were detected
in the hayfield or the pasture. E. coli disappeared more rapidly in the injected soil than on
the sludged grassland, where it persisted through several months of freezing weather. E. coli
was commonly found on the pasture but was found only once on the control hayfield. The
authors suggest that a winter is necessary to lower levels of Enterobacteriaceae.
3-42
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A range of IC^-IO11 Salmonella organisms has been reported to be the infective dose
for calves, with "1011 organisms needed for infection in adult cattle (Jones, 1983). The
infective dose for sheep is >108 organisms.
Taylor and Burrows (1971) conclude that the chance of contaminated pasture proving
infective for cattle depends on the concentration and persistence of organisms and the
interval between application and grazing. In experiments where Salmonella dublin persisted
in the soil for 7 days after application to pasture in a slurry of 106 organisms/g, calves
became infected; however, no calves were infected from pasture to which a slurry of 103
organisms/g had been applied, and no isolates were obtained from soil or pasture.
The results of a study by Kampelmacher and van Noorle Jansen (1974) in the
Netherlands suggest that cattle grazing on contaminated pasture within 1-3 weeks of sludge
application may become infected with Salmonellae. They isolated Salmonellae from 4.4%
of fecal samples collected randomly from pasture and barn areas where cattle had been
pastured on fields sprayed with anaerobically digested sludge. In the absence of field
controls, the authors compared this rate to the rate of recovery of Salmonellae in a
slaughterhouse in previous studies, 0.3-0.6%.
Hess and Breer (1975a) and Hess (1981) cited many cases of salmonellosis in cattle
grazing sludge-amended pastures. Strong evidence for a relationship between applied sludge
and infection was presented by a case in which Salmonella tokoin was recovered 7 weeks
after sludge application from sick cattle and from the grass of the sludged field to which
they had been exposed at 4 weeks after sludge application. No controls were reported.
Hess and Breer (1975a) also note that during the period from 1969-1974 peak incidences
of Salmonellae isolation from cattle corresponded to the sludge application and annual
grazing seasons.
Jones (1983) reviewed the infectivity of contaminated pasture to grazing animals and
suggested that the infection of cattle from Salmonella on pasture is unlikely unless cattle are
highly susceptible or exposure to a high concentration is extremely prolonged. In a number
of reports involving the spread of slurry inoculated with Salmonella or the spread of
naturally infected sewage, no animals (cattle, sheep, goats, guinea pigs, mice and moles)
3-43
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became infected. However, isolates of Salmonella dublin were taken from calves grazing on
pasture soon after application of a very high rate of contaminated slurry (Thomas and
Harbourne, 1974). Reports from Switzerland, Germany, Holland and England indicate that
spreading large amounts of sewage sludge has been associated with Salmonella infection in
cattle (Jones, 1983).
i , ' '
According to Golueke (1983) in his review of some of the literature on bacterial
survival on crops, pathogenic bacteria typically are unable to penetrate the intact skin of
fruits or vegetables although they can enter through the open stomata. If bacteria gain
access to the plant through the stomata, removal by washing is quite difficult. Larkin et al.
(1978) point out that since bacteria can survive longer than the time required for
distribution to the consumer and longer than some crop-growing seasons, it is prudent to
prohibit the use of sewage wastes, unless proven pathogen-free, to irrigate or fertilize food
crops that enter homes or restaurants in the raw state. They caution that bacterial die-off
on crops and in soil is a rate phenomenon determined by environmental conditions and
initial contamination levels.
Rudolfs et al. (1951) found that the survival of Salmonella and Shigella on tomatoes
did not exceed 7 days and E. coli, even applied directly to the fruit, decreased to levels
equivalent to or below controls in <35 days. They conclude that cessation of sewage
application one month before harvest would offer a safety margin in protecting against
enteric bacterial disease.
Feachem et al. (1983) summarize bacterial pathogen survival times on vegetables,
noting that they are relatively short in relation to survival in other environments:
• Salmonella spp. <30 but usually <15 days
• Shigella spp. <10 but usually <5 days
• Vibrio cholerae <5 but usually <2 days.
Salmonella vary in their die-off times on specific crop types but generally survive <53 days
on root crops, <40 days on leafy vegetables, <5 days on berries, >2 days on orchard crops
and 10-37 days on radishes and lettuce. Vibrio cholerae survival times on fruits and
3-44
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vegetables were typically <7 days except at temperatures in the range of 2-5°C when survival
times of as much as 2-4 weeks were possible (Feachem et al., 1983).
Sadovski et al. (1978) suggested that modification of the drip method of irrigation
might lower the risk of bacterial contamination of vegetables grown in desert fields irrigated
with treated sewage effluent. Fecal coliform densities were higher (38 fold) in washings of
vegetables (cucumbers and eggplants) that were grown in drip-irrigated fields than in
washings from vegetables grown in control fields irrigated with fresh water. Covering the
soil with plastic sheeting during irrigation with sewage reduced the fecal coliform
contamination by >20 fold and sub-surface irrigation with covered soil further reduced fecal
contamination. When drip irrigation with sewage was halted at flowering with subsequent
irrigation using fresh water, fecal coliform levels were similar to those in the control field.
3.4. TRANSPORT
During spray application of sludge to soil, pathogens from sludge may be transported
as aerosols, suspensions of solid or liquid particles, up to "50 urn in diameter. The
concentration of microorganisms in the particles will depend upon the spray process used
and the initial concentration of organisms in the sludge (Schaub et al., 1978). Following
application to soil, pathogens may be carried offsite in runoff (horizontal transport), possibly
contaminating surface water, or they may percolate through the soil (vertical transport) to
the groundwater. Horizontal transport of pathogens in the soil can be monitored by
intercepting the runoff at some point or by analysis of the surface water impacted by the
runoff. Vertical transport is largely concerned with contamination of the groundwater.
3.4.1. Transport in Soil. Since many Americans, especially in rural areas, obtain their
drinking water from groundwater, it is important that application of sludge to soil does not
contaminate the groundwater supplies. One-third of individual groundwater sources in rural
Oregon were found to be fecally contaminated (Lamka et al., 1980). Craun (1979) reports
that between 1971 and 1977 "50% of the outbreaks of waterborne disease in the United
States were attributed to contaminated groundwater. Both Liu (1982) in Canada and Nell
et al. (1981) in South Africa have concluded that the possibility of bacterial contamination
3-45
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of groundwater from agricultural application of sludge is small if the water table is not high
and the soil is well drained.
Hagedorn (1984) concludes that both physical removal and inactivation influence the
survival of bacteria in contaminated wastewaters moving through the soil. He summarized
existing information on bacterial transport: (1) bacterial movement is limited to a few
dozen cm when carried by percolating water in unsaturated soil, but under saturated flow
conditions much longer distances are possible; (2) bacterial retention by soil is inversely
proportional to component particle size in the unstructured matrix; (3) filtration of
microorganisms by soil particles is the major limitation to bacterial transport through soils,
and sedimentation of bacterial clusters occurs in the saturated zone; (4) adsorption of the
bacteria by soil becomes more effective as soils contain a greater clay percentage; and (5)
die-off or inactivation of the bacteria takes on more importance under conditions of
unsaturated flow or extended retention of the bacteria.
According to Gerba and Bitton (1984), bacterial movement through soil is
determined by filtration and adsorption; and Moore et al. (1988) concur that filtration and
adsorption have the most effect on bacterial removal in soil. Filtration is a function of the
size and shape of the bacteria and the soil characteristics. Size and shape of bacteria affect
how well they move through the soil as well as their rate of movement. In general, larger
bacteria, if not adsorbed, will move through the larger soil pores that allow greater velocities
(Yates and Yates, 1988). Finer grained soils, such as clays and loams, remove more bacteria
than coarse-textured sands and gravels. Coarse, gravelly or fissured soil allows more
organisms to pass through. Glotzbecker and Novello (1975), using soil columns of sand and
clay, showed that >99% of applied bacteria were trapped in the soil, with clay having the
highest removal efficiency. Likewise, Hagedorn et al. (1981) indicated that clay soils were
the most effective in retaining bacteria. Smith et al. (1985) found that the filtering action
of disturbed soil was greater than that of undisturbed soil and conclude that disturbing the
soil closes macropores and paths of movement. Organic matter also affects migration by
forming a mat or clogged zone consisting of bacteria and extracellular materials. Butler et
al. (1954) describe a second filtering mat at 10-50 cm formed by the accumulation of
bacterial cells in the soil.
3-46
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In order to study the effects of filtering and adsorption by the soil matrix on the
movement of bacteria, Alexander et al. (1991) ground, seived and packed loam soil into
columns to eliminate channeling through macropores. The influences of cell properties such
as size, presence of capsule, electrostatic charge and hydrophobicity on transport were
examined. Bacterial cell size was statistically related to transport, larger cells moving less
in the soil than smaller ones. Sorption also correlated with transport; when strong,
adsorption retarded movement through the soil columns.
Adsorption prevents movement of bacteria into groundwater, and as clay content of
the soil increases, adsorption of bacteria increases (Hagedorn and McCoy, 1979). Clay soils
promote greater surface adsorption. Cations such as Ca2+, Mg2*, and Na+ reduce bacterial
movement through the soil by neutralizing negative bacterial charges, thereby promoting
adsorption to soil particles (Yates and Yates, 1988). Goldschmid et al. (1973) reported that
increased pH values increased the adsorption of coliform bacteria to soil particles by
reducing the repulsion between the two negatively charged forces.
Alexander et al. (1991) observed that bacterial movement in columns of homogenous
sandy aquifer material increased when the ionic strength of the inflowing solution was
reduced. Transport also increased with an increase in flow velocity and with high cell
concentrations. High flow velocities may reduce bacterial "reaction" time since retention in
the soil is reduced; at high cell concentrations, the available retention sites in the soil may
be filled so that additional organisms moved freely through the columns.
Bacterial migration is also influenced by the amount of liquid applied to the soil and
the rate at which it is applied. With high rates of application, the probability of adsorption
decreases since the liquid moves the organisms rapidly through the soil. Sagik et al. (1978)
report that adsorption is reversible and rainwater may move the bacteria through the soil.
Gerba (1985) reported that bacterial contamination of groundwater coincides with heavy
rainfall.
In their study of the transport in soil, Alexander et al. (1991) found marked strain
differences in the capacity for movement among 19 bacterial strains selected for their ability
to degrade chemicals as benzene. The strains tested included members of the genera that
encompass many of the pathogenic bacteria of minor concern. However, there was no
3-47
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consistent pattern in mobility among the strains of a genus. Transport ranged from 0.01-
15% of added bacteria.
Moore et al. (1988) and Crane et al. (1983) summarize the factors affecting transport,
including infiltration and leaching, of bacteria in soil:
Soil physical characteristics
texture
particle size distribution
clay type and content
organic matter type and content
pH
cation exchange capacity (CEC)
pore size distribution
Soil environmental and chemical factors
temperature
moisture content
soil water flux (saturated versus unsaturated flow)
chemical makeup of ions in the soil solution and their concentrations
bacteria density and dimensions
nature of organic matter in waste effluent solution (concentration and size)
In experimental studies in the 1930s, E.coli was reported to have been transported
vertically for 3-9 m in the soil arid horizontally as far as 122 m (Viraraghavan, 1978). Chen
(1988) examined the fate of wastewater effluents and determined that the main factors
influencing groundwater contamination are the depth to groundwater from ground surface
and distance from sewage discharge to groundwater. The majority of highly contaminated
samples were collected within 30.5 m of sewage discharge and from groundwater <122 cm
below ground surface.
A minimum thickness of unsaturated soil, generally 2-3 m, is usually required beneath
septic fields to prevent the migration of high numbers of bacteria into groundwater (Yates
and Yates, 1988). Hagedorn et al. (1981) indicate that a thickness of 30-90 cm of soil
removed bacteria from septic tank effluent and, under unsaturated flow conditions, reduced
fecal coliforms and total coliforms to background levels. Hagedorn et al. (1981) compared
several studies and concluded that bacteria move greater distances under saturated
3-48
-------
conditions. Movement in unsaturated soils is largely by infiltration,, but groundwater flow
controls movement in the saturated zone.
Liu (1982) found that after 4 years of heavy sludge application, 90% of surviving
sludge bacteria were retained in the upper 20 cm of soil. Gerba et al (1975) reported that
as many as 92-97% of coliforms have been filtered out by the first centimeter of soil where
sunlight, oxidation, desiccation, and predation and antagonism from the soil microbes
contribute to die-off. Studies by Edmonds (1976) and Lue-Hing,et al. (1979) found that
fecal coliform levels in groundwater were not impacted by sludge application to soil.
E. coli inoculated into subsurface soil survived in appreciable numbers in cool wet
soil throughout a 32-day period (Hagedorn et al., 1978). Bacteria injected into subsurface
soil moved long distances in a relatively short period for soil with a surface gradient of only
2% (as far as 500 cm in 24 hours). Peak populations in the sampling wells were closely
associated with the rise of the water table following major rainfall periods. The rate of
subsurface flow and the movement of bacteria was faster in silt loam than silty clay loam.
t • - - . i • -
E. coli strains were injected into soil on a hill slope with an elevated water table
(McCoy and Hagedorn, 1979). Maximum concentrations of these organisms decreased with
distance downslope from injection. Initially, large reductions in numbers occurred as the
bacterial populations entered the soil system. The major portion of subsurface transport of
these populations occurred in specific zones in the soil profile and at an apparent maximum
velocity of 17.0 cm/min. Once these highly conductive zones were reached, relatively long
distances were necessary for further reduction in densities. The authors conclude that
pathogens may be transported rapidly horizontally to surface receiving waters or vertically
to aquifers. McCoy and Hagedorn (1979) found that, under conditions of saturated flow,
E. coli traveled in drainage water more than 20 m from drainage tile.
Sorber and Moore (1987) reported on the laboratory experiment of Heyward (1983).
Soil cores (46 cm deep) were spread with sludge and periodically watered. The percolate
water was examined for microorganisms. At the end of several "rainfall" cycles, no
Salmonellae had been detected in the percolate, but the sludge on top of one core yielded
70 organisms/100 g of sludge. No Shigella spp. were found in either sludge or percolate
3-49
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water. Following some rainfall events, Yersinia spp. and fecal coliforms were found in the
percolate water of several cores at levels of 4-61 organisms/100 mL of the percolate. At
the end of the test, Yersinia concentrations were an order of magnitude higher in the sludge
layer than at the beginning of the test. In the percolate water, the maximum level of
bacteria found was 0.1% of the fecal coliforms in the applied sludge. Despite the depth of
the soil, some bacteria were carried through the soil in this worst case situation by the heavy
"rainfall" (46 cm applied over 3 weeks).
White (1985) and White et al. (1986) have developed a model that describes solute
transport through soil. The model uses a two-component mechanism that involves "fast
solute transport through large, interped voids and slow to negligible transport through small,
intraped voids" (White et al., 1986). Experiments using suspensions of E. coli in water
applied to undisturbed soils were evaluated using the transfer function equations. Germann
et al. (1987) have also addressed the issue of transport of bacteria through porous soils.
Their results revealed that naturally occurring precipitation events resulted in minimal
transport of microbes deeper than 2-3 m in soil, although prolonged or very intense storm
events may carry microbes more than 100 m into the vadose zone. These methodologies
differ from that currently used to model groundwater transport in the Pathogen Risk
Assessment Model.
Moore et al. (1988) and McCoy and Hagedorn (1979) compiled data on bacterial
transport in soil. Table 3-9 uses those data to generate representative bacterial transport
rates. Although it is possible to generate transport rates from these data, the variability in.
soils and in conditions makes any definitive rate of limited applicability. Perhaps the
conclusion to be drawn from the available data is that transport of bacteria in soils is a
function of soil type and whether that soil is saturated or unsaturated. The consensus
appears to confirm that under unsaturated conditions in soil that is physically homogenous,
bacterial movement is limited to a few meters.
3.4.2. Transport in Surface Runoff. There have been few studies on the transport of
microorganisms in runoff from sludge-amended fields. Dunigan and Dick (1980) report that
elevated levels of bacteria were recovered from runoff as long as viable bacteria were
3-50
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TABLE 3-9
Bacterial Transport
Material
Sewage trenches
intersecting
groundwater (Stiles and
Crohurst, 1923)
Sewage in pit latrine
intersecting
groundwater (Caldwell
and Parr, 1937)
Tertiary treated
wastewater in
percolation bed
(Merrell, 1967)
Primary sewage injected
subsurface (Krone et
al., 1958)
Inoculated water and
diluted sewage injected
subsurface (Allen and
Morrison, 1973)
Inoculated effluent in
tile line (McCoy and
Hagedorn, 1979)
Water droplet aerosols
(Burge and Marsh,
1978)
Organisms
Bacillus coli
Bacillus coli
Fecal coliforms
and fecal
streptococci
Fecal coliforms
Bacillus
stearothermophilis
Escherichia coli
Bacteria
Medium
Find sand
Sand and
sandy
Coarse
gravels
Sand and
pea gravel
Crystalline
bedrock
Silty clay
loam
Wind
6.7 mph
11 mph
Measured
Distance
Traveled
19.8m
(65ft)
10.7m
(35ft)
457.2 m
(1500 ft)
30.5m
(100ft)
28.7m
(94 ft)
20m
(65ft)
150-180 m
330-430 m
Travel
Time
27
weeks
8 weeks
15 days
35
hours
24-30
hours
5 hours
Rate
(calculated)
0.10 m/day
(0.34 ft/day)
0.19 m/day
(0.63 ft/day)
30.5 m/day
(100 ft/day)
20.9 m/day
(68.6 ft/day)
28.7 m/day
(94 ft/day)
96 m/day
(312 ft/day);
max rate
measured:
17 cm/min=
244.8 m/day
(803 ft/day)
*Source: Moore et al., 1988; McCoy and Hagedorn, 1979
3-51
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present in sludge-amended fields. Studies by Edmonds (1976) and Lue-Hing et al. (1979)
found no impacts to surface water from land application of sludge.
Biskie et al. (1988) found that under summer flow C9nditions, 95% of indicator
organisms in manure from rangeland-grazed cattle settled out of the flow within the first
50 m from point of deposition. However, Dutka and Kwan (1980) examined the survival
and stream transport rate of an indicator species (Serrdtia marcescens) and found that some
members of the family Enterobacteriaceae could survive 22 days and be carried as far as
20 km downstream. Even under summertime low-flow conditions, survival of 8 days and
transport of 5.5 km were possible. Their data also indicate that no predetermined transport
rate in a stream can be established, since the bacteria do not travel at the same rate as the
water due to sedimentation, adsorption, or water flow characteristics.
3.4.3. Transport in Groundwater. The filtering action of the soil normally restricts
horizontal bacterial movement to a few hundred feet unless coarse soils or channels exist
(Sorber and Outer, 1975), and rate of movement, although site-specific, is usually slow.
However, survival tune in groundwater may be longer since conditions such as moisture,
temperature, pH, and absence of sunlight and other microorganisms may be favorable.
According to Matthess and Pekdeger (1985), the velocity of groundwater ranges from
<1 m/day or a few meters/day in porous aquifers (sand and gravel) to 0.3-8000 m/day in
hard rock aquifers and <26,000 m/day in karstic aquifers, indicating that the larger diameter
flow paths in hard rock aquifers are more conducive to transport of suspended
microorganisms. At the sediment-water boundary the biologically active layer of sorptive
small particles and microbial slimes is very effective, essentially filtering the bacteria and
preventing their migration to the aquifer. At high bacterial concentrations, flocculation and
aggregation limit transport into the aquifer.
Champ and Schroeter (1988) assessed bacterial transport in groundwater through
fractured rock, finding that bacteria can move more rapidly than conservative tracers used
to quantify the speed of water movement. Filtration of bacteria and non-reactive particles
was observed, but the authors conclude that the use of typical conservative tracers for water
movement could result in significant underestimates of potential exposure to particulate
contaminants (including bacteria) from consumption of water from wells in fractured media.
3-52
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Bacteria are limited in their movement by groundwater not only by filtration but also
by adsorption. The continuous adsorption-desorption reactions cause a delay in the
movement of the bacteria relative to the movement of the groundwater. This retardation
of the microorganisms relative to groundwater flow means that inactivation processes may
have more time to affect bacteria in the saturated subsurface. However, Matthess and
Pekdeger (1985) suggest that the effects of sedimentation have been overestimated for
bacteria. They indicate that the kinetic energy of a small particle being transported by
groundwater to the surface of a soil particle is insufficient to overcome the repulsing surface
forces.
A study of groundwater following disposal of sewage by injection at 15 cm (6 inches)
depths showed no fecal coliforms detected in samples from wells 30.5 m away in the
direction of groundwater flow or from wells located directly beneath the site (Johnson and
Urie, 1976).
3.4.4. Transport in Air. Adams and Spendlove (1970) showed qualitatively that aerosolized
coliform organisms can be emitted by sewage treatment facilities; samples taken as far as
1.2 km (0.8 mi) downwind were positive for coliforms. Factors improving the survival of E.
coli in aerosols generally include high wind velocity and relative humidity, darkness and low
temperatures.
Burge and Marsh (1978) recorded distances traveled by bacteria in aerosols that
ranged from 150-180 m in a 6.7-mph wind to 330-430 m with an 11-mph wind. They also
reported that a 50 nm water droplet evaporates in 0.31 second in air at 23°C and 50%
relative humidity. Ultraviolet radiation and desiccation hastened die-off of bacteria in
aerosols. However, Sorber and Outer (1975) note that while E. coli is short-lived in
aerosols, encapsulated Klebsiella species are protected against desiccation.
Katzenelson and Teltch (1976) and Katzenelson et al. (1977) investigated the
dispersion of enteric microorganisms associated with spray irrigation using wastewater. They
found coliforms 30 m downwind of an aerated sewage pond and 350 m downwind from a
wastewater spray sprinkler. However, aerosolized E. coli were detected only when the
concentration of bacteria in the sprayed effluent was 104/mL. Only one colony of
3-53
-------
Salmonella bacterium was detected (Salmonella infantis at a distance of 60 m downwind),
but the authors point out that Salmonellae are far less ubiquitous than coliforms in sewage.
They calculate that a worker 100 m from a sewage sprinkler would inhale "36 coliform
bacteria in 10 minutes.
The Lubbock study (Camann et al, 1988) found that wastewater aerosols elevated
microorganism densities (including fecal coliforms and fecal streptococci) significantly above
background levels for at least 200 m downwind. The authors determined that 40% of the
viable particles were in the size range for efficient pulmonary deposition.
3-54
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4. PARAMETERS FOR MODEL RUNS
4.1. RATIONALE FOR PARAMETER SELECTION
The assessment of human health risk from pathogenic bacteria as a result of land
application of sewage sludge requires a realistic description of the fate and transport of the
pathogens. A considerable amount of information has been found in the published
literature describing the survival of bacteria, both pathogens and indicator organisms, during
the sewage treatment process and in soil. However, there appear to be few quantitative
measures of their movement in soil or their inclusion in aerosols, and the number of
published die-off rates in these media is limited. In this analysis, it is assumed that bacteria
are transported into subsurface soil and subsequently into groundwater and are included in
any droplet aerosols formed by spray application, as well as in any particulate aerosols
formed by disturbance of the soil by wind or by cultivation. It is also assumed that the
bacteria die at a characteristic rate that depends on the ambient temperature and the
medium in which they are found; thus, there are different die-off rates for the same
organism in moist soil, dry particulates, droplet aerosols and water.
Appendix A presents an overview of the Pathogen Risk Assessment Model, and
Section 4.2 provides a list of the main program parameters, their definitions and values.
Main program parameters used in the model runs were varied over, a range of values
to determine the sensitivity of the model to variations in conditions. In general, the default
value of a given parameter was compared with a reasonable higher and a reasonable lower
value, where the high and low values were taken from available literature or estimated when
literature values were not available. Results of each model run were compared with a
standard model run (file root designation 0102); this run included nondefault values where
the default values had been shown to give unrealistic results. A summary of the changes
from default values is given in Section 4.2; generally, the nondefault values are those
affecting the timing of crop presence, harvesting and cultivation and transfer of bacteria
from the soil surface to the crop. In addition, the concentration of bacteria in sludge was
increased to 5xl06 to ensure a high enough probability of infection in the major exposure
compartments to allow comparisons to be made.
4-1
-------
Survival of bacteria appears to be particularly sensitive to temperature. Over a period
of several years, the mean air temperature at any recording location follows an annual cycle
described by a sine curve with a maximum in July and a minimum in January (U.S. EPA,
1989a). Thus the mean air temperature on any given day is calculated from the date and
the minimum and maximum average annual temperatures at the site. An example of this
calculation is shown in the model conceptualization (U.S. EPA, 1989a).
Not enough information is available at this time to construct a comprehensive
mathematical description relating bacterial die-off to environmental parameters including
temperature, moisture and chemical milieu. For this model, the relationship between die-off
rate and temperature was derived empirically. It was assumed that an equation generated
by regression analysis of reported data could be used to predict die-off rates at various
temperatures. For this analysis, log-transformed rates were fitted to the die-off equation;
because die-off is an exponential function, the resulting equation was:
log(SURV) . .10
(SLOPES * TEMP '
where:
SURV
SLOPE
NTRCP
hourly reduction in survival
slope of the inactivation equation
intercept of the inactivation equation.
Because the model must be flexible in response to new information, separate equations were
derived for moist soil and dry particulates; the parameters were named SLOPES [P(37)] and
NTRCPS [P(38)] for moist soil and SLOPEP [P(39)] and NTRCPP [P(40)] for dry
particulates. The slopes and intercepts of the die-off equations are entered as parameters
for each model run. To generate alternative values for the present sensitivity analysis, the
slope was increased or decreased by a factor of two, and the intercept was adjusted by trial
until the die-off rate at 20°C remained the same. To illustrate the effect of the new slopes
and intercepts, the survival after 24 hours was calculated, using the equation given above.
The results of these calculations are shown graphically in Figure 4-1. It is clear from this
4-2
-------
o
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o
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=
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(N
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4-3
-------
result that the slope and intercept of the inactivation curve are important in determining the
type as well as the extent of the response of die-off to temperature.
In the test model runs, a number of combinations of parameters were used, to simulate
a variety of die-off rates for moist soil and dry particulates. The combinations of slopes and
intercepts used in the initial model runs are described in Table 4-1.
As alternatives to temperature-sensitive die-off rates, specific temperature-independent
die-off rates may be specified. To test their effect on the calculated risk of infection,
temperature-insensitive values were substituted during some of the model runs. Most
reported die-off rates range from 0.001-0.02 logs/hr (Tables 3-6 through 3-8); these values
were substituted to test the die-off algorithm as indicated in Table 4-2.
A number of parameters are interdependent. For example, the rate and depth of
irrigation combined should not exceed the infiltration rate and moisture-holding capacity of
the soil; otherwise, runoff of irrigation water will occur. In the present test, no attempt was
made to prevent combinations of parameters resulting in a simulation of runoff as a result
of excess irrigation. For parameters directly affecting specific crops or animal feeding
practices, the relevant practices must be specified when those parameters are varied. For
example, when testing FCROP1 [P(46>] and FCROP4 [P(49>], both of which determine
transfers to an aboveground crop, the aboveground crop option (CROP [P(66)] = 1) must be
used. Similarly, some transfers are not relevant to all practices, so the parameters governing
them need not be tested in all practices. Groups of parameters were changed in some
model runs as indicated in the tables of input values.
42. PARAMETER VALUES
An initial sensitivity analysis was performed using site-specific parameters for Site 1,
Anderson County, TN. In this analysis, parameters were systematically varied to simulate
a wide variety of possible application methods, agricultural practices and other conditions.
The parameters examined included those of the main program, Subroutine RISK,
Subroutine GRDWTR and Subroutine RAINS. The ranges of values and the rationale for
their selection are discussed below.
4-4
-------
TABLE 4-1
Test Values of Parameters for Die-Off Equations
Parameter Name
Number
Model Run
1
2
3
4
5
6
7
8
9
SLOPES
P(37)
0.0206
0.0103
0.0412
0.0206
0.0103
0.0412
0.0206
0.0103
0.0412
NTRCPS
P(38)
2.113
1.90
2.525
2.113
1.90
2.525
2.113
1.90
2.525
SLOPEP
P(39)
0.00449
0.00449
0.00449
0.00225
0.00225
0.00225
0.009
0.009
0.009
NTRCPP
P(40)
1.435
1.435
1.435
1.39
1.39
1.39
1.525
1.525
1.525
4-5
-------
TABLE 4-2
Values of Temperature Parameters
Model Run
1
2
3
4
5
6
7
8
9
10
Value of Parameter
TMAX P(35)
Maximum Temperature
24.8
10
40
24.8
10
40
24.8
10
40
2.8
TMIN P(36)
Minimum Temperature
2.8
2.8
2.8
-30
-30
-30
15
15
15
2.8
4-6
-------
4.2.1. Main Program Parameters. The values of program parameters used in the initial
study are given in Table 4-3. The default values are given in bold-face type. Alternative
values were chosen to represent a reasonable range for each of the parameters, in order to
test the sensitivity of the model to variations in the ranges. Some of the values were also
changed in combination with values of related parameters to test the interactions among the
related parameters. These groups of parameter values are listed in Tables 4-4 - 4-6.
42.2. Parameters for Subroutine RISK. Subroutine RISK performs calculations of
exposure to pathogens in the various exposure compartments. Parameters specific to
Subroutine RISK describe processing methods, volume of the onsite pond, distance of the
offsite receptor from the aerosol source and daily contact with and ingestion of
contaminated soil and crops. The parameters used in the model runs are listed in Table 4-7
(default values are given in bold-face type).
4.2.3. Parameters for Subroutine GRDWTR. The rate of subsurface transport depends on
a number of parameters, including the effects of soil structure and chemistry on movement
of water and suspended particulates, in this case bacterial pathogens. These effects are
described in the modeling subroutine in terms of groundwater velocity, retardation
coefficient, and hydrodynamic dispersion coefficient. The distance to the receptor is also
significant, because the pathogens disperse, are diluted, and die off over time. In the initial
model runs, parameters describing subsurface transport of bacteria in groundwater were
varied over a range of values described in Table 4-8. These values may not accurately
reflect the true range of values that might be encountered under natural circumstances,
because of the wide variability of soil types and the heterogeneity of soil structure at any
actual site even with the same soil type. However, as discussed in Section 6.1, the most
significant risk factor for this subroutine is distance of the offsite well from the source of
bacteria. The lowest value for this distance (20 m) was chosen as an unrealistic but
conservative lower limit to indicate the sensitivity of the model to distance.
Although distance to groundwater may also be significant in determining the fraction
of pathogens transferred from the surface to groundwater, the model does not contain a
subroutine for modeling transport in the unsaturated soil zone, so transfer of pathogens to
4-7
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o
4) J2
t/j /O
Low infective do:
Default value.
Higher infective
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O X
r— 1 i—< T— I
0)
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a
Js,
I
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ft\
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of pathogen:
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Rainfall is the m
runoff/sediment
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Fraction of i
contaminate
DILIRR
oo
1— 1
f
A\
No irrigation.
Default, twice we
O «SI
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1
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en
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C/3
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3
13
^^
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irrigation
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^
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1 -:
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®
Very high rate of irrigation.
T—l
T-l
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Limited depth of irrigation (en
Default value.
1/3
If
o
a
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!
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W:
Q.
a
Extensive irrigation.
o
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to
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i concenti
i water
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g -
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Very early windstorm.
Default early windstorm.
^o ^&
CO ^
1
'5b
i
2
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3
B
1
P
Q
H
«
Delay windstorm to 180 days.
o
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Brief windstorm.
Default value.
r^
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2
of winds
§
1
°
1
Q
^
>>
'O
1Q
Windstorm of long duration (i:
8
en
_C3
Very mild windstorm (-16 mp]
Default value (-40 mph).
Strong windstorm (-60 mph).
t-22^
2
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1
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Rationale for Values
t/j
3
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1
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o
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VI ~
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0
73
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8
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Height of pi
tilling (m)
W
oo
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1
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t/3
>£•
1
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Critical wim
CO
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rd
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a,
>4
T3
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a
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t;
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OH
are surface runoff/sediment trans
I
o
£
"8
1
u
8
•e
g
'B
Fraction of
COVER
o
£
'Sf
1
oil and soil with vegetation (mate
in Subroutine RAINS).
si
11
OS
O
vegetation
•a
3
^
are default value to an unrealistic
a
*T
0
(«
to determine whether the model
ve to offsite aerosols.
S? o
> V)
m cs
O O
g
1
P
&
1
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<*S
Efficiency o
AEREFF
TH
m
.,
o
a,
2
:o determine sensitivity of air trar
during irrigation to windspeed.
11
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CS It l> TH
1
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pa
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m
N
m
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ro
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Default value.
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Intercept of inactivi
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Arbitrary lower valu
Default value.
Arbitrary upper valu
m os
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na
§
$ fr
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Slope of inactivatio
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0-
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4-13
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dnimum possible.
, maximum possible ti
a g
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Arbitrary lower
Default value.
n
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0
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o
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cf
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£.-
u.
f §
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Default value.
Arbitrary upper
rj
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to '3 to
x <*i x
r^5
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CA •
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subsurface.
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4-15
-------
9
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E
3
a
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$
3
13
t_
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Rationale
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Q
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Arbitrary lower value.
Default value.
T-H
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3
oo
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ta o3
a s
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,
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Arbitrary lower value.
Default value.
Arbitrary upper value.
CS 1/5 t~-
O 0 0
13
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g*
g,
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11
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Default value.
Arbitrary upper value.
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Default value.
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4-20
-------
TABLE 4-4
Input Parameters for Effect of Irrigation and Crop Type
on Probability of Infection
Model Run
1
2
3
4
5
6
7
8
9
10
11
12
Value of Parameter
IRMETH
P(17)
0
0
0
0
0
0
1
1
1
1
1
1
CROP
P(66)
1
1
0
0
-1
-1
1
1
0
0
-1
-1
NIRRIG
P(19)
2
7
2
7
2
7
2
7
2
7
2
7
4-21
-------
TABLE 4-5
Input Parameters for Garden Crop Type
Model Run
1
2
3
4
5
6
7
Value of Parameter
PLNT1
P(74)
0.4
0.1
0.1
0.8
0.1
0.4
0.4
PLNT2
P(75)
0.3
0.1
0.8
0.1
0.4
0.1
0.5
PLNT3
P(76)
0.3
0.8
0.1
0.1
0.5
0.5
0.1
4-22
-------
TABLE 4-6
Values of Cattle Feeding Parameters
Tkyr.-..J_1
Model
Run
1
2
3
4
5
6
7
8
Value of Parameter .,....'
CATTLE
P(78)
1
1
1
1
-1
-1
-1
-1
STORAG
P(80)
0
0
1440
1440
0
0
1440
1440
FORAG
P(81)
75
75
25
7
75
75
25
7
ALFALF
P(82)
80
80
30
10
80
80
30
10
SCNSMP
P(83)
•, 2.5
2.5
1.1
0.25
! 2.5
2.5
1.1
0.25
FATTEN
P(84)
5760
720
720
720
5760
720
720
720
TSLOTR
,P(85)
240
30
90
90
240
30
90
90
4-23
-------
TABLE 4-7
Parameters* for Subroutine RISK
Parameter
# Name
5 DRECTC
(g/day)
6 DRECTS
(g/day)
8 ICAN
11 IFREE
29 TSTM2
(hours)
32 TSTR7
(hours)
33 VOLPND
(m3/ha)
34 XDIST
(m)
35 YDIST
(m)
Value
0.02
0.1
1.0
0.02
0.1
1.0
0
1
0
1
360
720
2880
360
720
2880
2X101
IxlO2
IxlO3
100
200
0
10
30
Rationale for Choice of Values
Determine effect of amount of contaminated crop
surface ingested during routine daily work in the
field.
Determine effect of amount of contaminated soil
ingested during routine daily work.
Default, for fresh vegetables.
Flag for canning sequence.
Default, for fresh vegetables.
Flag for freezing sequence.
Reduced storage time of meat before processing.
Default value.
Increased storage time of meat before processing.
Reduced storage time of vegetables after
processing.
Default value.
Increased storage time of vegetables after
processing.
Very small onsite pond.
Default value.
Increased value for pond.
Determine effect of distance to receptor of offsite
aerosol.
Determine effect of lateral distance of receptor
from centerline of aerosol plume.
*Default values are in bold-face type.
4-24
-------
TABLE 4-8
Parameters* for Subroutine GRDWTR
Parameter
# Name
; 2V
3 D
4 R
9 XI
10 DX
11 XM
Definition
Velocity
(cm/hr)
Dispersion
coefficient
Retardation
coefficient
Starting
distance (m)
Distance
increment (m)
Maximum
distance (m)
Value
0.9
3.6
10.8
20
60
100
0.2
1.0
2.0
20
50
200
20
50
200
20
50
200
Rationale for
Choice of Values
Arbitrary lower value.
Default value. ',
Arbitrary upper value.
Arbitrary lower value.
Default value.
Arbitrary upper value.
Arbitrary lower value.
Default value.
Arbitrary upper value.
Arbitrary lower value.
Default value.
Arbitrary upper value.
Arbitrary lower value.
Default value.
Arbitrary upper value.
Arbitrary lower value.
Default value.
Arbitrary upper value.
*Default values are in bold-face type.
4-25
-------
groundwater must be specified by the parameter FRGRND [P(53)]. Alternatively, the effect
of a soil structure that readily allows contamination of groundwater can be tested by
specifying an initial concentration of pathogens in the groundwater compartment. To
simulate transport to an offsite well from a contaminated aquifer, a test run was made using
standard default parameters, but specifying an initial concentration of IxlO6 pathogens/L.
Results of these tests are discussed in Section 6.2.4.
4.2.4. Parameters for Subroutine RAINS. The Modified Universal Soil Loss Equation,
which is the basis for Subroutine RAINS, uses input parameters that describe soil type,
topography, and land-use practices. Therefore, the input parameters for this subroutine
depend on the site being modeled. A variety of locations, representing different soil types,
topography and climate, were selected for application of the model in a previous analysis
(U.S. EPA, 1989a). The same sites were used for the present model runs. Because
Subroutine RAINS is limited to ten discrete rainfall events, the rainfall parameters are not
strictly accurate. However, the ten events modeled were included as early in the model run
as was reasonable for each site in order to maximize the effects of rainfall on surface runoff
and sediment transport.
The parameters for Subroutine RAINS are described in Table 4-9. Parameter values
used in the model runs are described in each site description (Chapter 5). Because
Practices IV and V do not include an onsite pond to receive surface water runoff,
parameters for Subroutine RAINS are given only for Practices I, II and III.
4-26
-------
TABLE 4-9
Parameters for Subroutine RAINS
Parameter Definition
# Name
2
3
4
-: 5
6
7
8
9
10
11
PDUR
PTOT
BTLAG
GN
AMC
STAD
USLEK
USLEL
USLES
USLEC
Duration of rainfall (hr)
Total rainfall (cm)
Basin time lag (hr)
Curve number
Antecedent moisture conditions
Storm advancement coefficient
USLE K value (soil credibility factor)
USLE L value (slope length factor)
USLE S value (slope steepness factor)
USLE C value (cover management factor)
4-27
-------
-------
5. SITES FOR MODEL RUNS
Six sites were chosen to provide a variety of soil types, topography and meteorologic
patterns. Other than Anderson County, TN, for which more detailed meteorologic data
were available to the authors, specific sites were chosen arbitrarily with the goal of
geographic diversity. Data on soil properties were taken from U.S. Soil Conservation
Service soil surveys, which have been developed for each county in the United States.
Meteorologic data were taken from the National Oceanic and Atmospheric Administration
Local Climatological Data Annual Summaries for 1981 (NOAA, 1981). The sites chosen
for the model runs are described below.
5.1. SITE 1: ANDERSON COUNTY, TN
Values of site-specific variables were chosen to reflect conditions at an agricultural
location in the Clinch River Valley of East Tennessee.
5.1.1. Description of Soil. The soil chosen for the model run is the Claiborne series, which
comprises fine-loamy, siliceous, mesic Typic Paleudults. It is further described as follows
(USDA, 1981a):
The Claiborne series consists of deep, well drained soils that formed in
sediment deposited by water or in residuum of dolomite. These soils are on
ridgetops, on hillsides, and at the base of slopes. The slope range is 5 to 45
percent, but in most areas the gradient is 12 to 30 percent...
The solum is more than 60 inches thick. Depth to dolomite bedrock is more
than 72 inches. The soil is strongly acid or very strongly acid throughout
except for the surface layer where limed. The content of coarse chert
fragments ranges from 5 to 25 percent in each horizon. These fragments
commonly increase in size and abundance with increasing depth.
Claiborne soils are of hydrologic group B, characterized by moderately low runoff potential,
moderate infiltration rates and moderate rates of water transmission.
For this analysis, it was assumed that sites with slopes > 10% (6°) would not be used
because of the likelihood of excessive runoff.
5-1
-------
5.1.2. Narrative Climatologic Summary. The following climatologic summary for Oak
Ridge, Anderson County, TN, was taken from NOAA (1981):
Oak Ridge is located in a broad valley between the Cumberland Mountains,
which lie to the northwest of the area, and the Great Smoky Mountains, to
the southeast. These mountain ranges are oriented northeast-southwest and ,
the valley between is corrugated by broken ridges 300 to 500 feet high and
oriented parallel to the main valley. The local climate is noticeably
influenced by topography. Prevailing winds are usually either up-valley, from
west to southwest, or down-valley, from east to northeast. During periods of
light winds daytime winds are usually southwesterly, nighttime winds usually
northeasterly. Wind velocities are somewhat decreased by the mountains and
ridges. Tornadoes rarely occur in the valley between the Cumberlands and
the Great Smokies. In winter the Cumberland Mountains have a moderating
influence on the local climate by retarding the flow of cold air from the north
and west.
The coldest month is normally January but differences between the mean
temperatures of the three winter months of December, January, and February
are comparatively small. The lowest mean monthly temperature of the winter
has occurred in each of the months December, January, or February in
different years. The lowest temperature recorded during the year has
occurred in each of the months November, December, January, or February
in various years. July is usually the hottest month but differences between the
mean temperatures of the summary months of June, July, and August are also
relatively small. The highest mean monthly temperature may occur in either
of the months June, July, or August and the highest temperature of the year
has occurred in the months of June, July, August, and September in different
years. Mean temperatures of the spring and fall months progress orderly from
cooler to warmer and warmer to cooler, respectively, without a secondary
maximum or minimum. Temperatures of 100° [38°C] or higher are unusual,
having occurred during less than one-half of the years of the period of record,
and temperatures of zero or below are rare. The average number of days
between the last freeze of spring and the first freeze of fall is approximately
200. The average daily temperature range is about 22° [12°C] with the
greatest average range in spring and fall and the smallest in winter. Summery
nights are seldom oppressively hot and humid. Low level temperature
inversions occur during approximately 57 percent of the hourly observations.
Fall is usually the season with the greatest number of hours of low level
inversion with the number decreasing progressively through spring and winter
to a summertime minimum but seasonal differences are small.
5.13. Temperature. The monthly average temperatures at this location ranged between a
low of 2.8° C and a high of 24.8° C.
5-2
-------
5.1.4. Rainfall. An hourly rainfall record for April and May, 1989, was obtained from the
Atmospheric Turbulence and Diffusion Laboratory, National Oceanic and Atmospheric
Administration, Oak Ridge, TN. Profiles of the first ten rain events beginning April 1, the
time the model run is initiated, were constructed from this record. Profiles consisted of the
duration of the event (PDUR), the total amount of precipitation in the event (PTOT) and
the storm advancement coefficient (STAD), which was determined by inspection of the
hourly precipitation. The resulting parameters were as follows:
Event
No.
1
2
3
4
5
6
7
8
9
10
START
ftirt
77
174
726
826
924
1180
1340
1549
1590
1650
PDUR
flirt
5
8
11
7
4
5
12
2
9
14
PTOT
(cm)
1.60
1.52
1.55
3.30
1.50
2.31
4.06
1.63
2.52
3.48
STAD
0.65
0.36
0.12
0.27
0.56
0.19
0.52
0.46
0.52
0,45
5.1.5. Parameters for Subroutine RAINS. Parameters for Subroutine RAINS were
modified to describe local rainfall and soil conditions for Anderson County, TN. Values
were calculated as described in Appendix B of U.S. EPA (1989a). The values used in the
model run were based on a field with dimensions 500 m by 200 m, sloping at an angle of
6° (10.5%). It was assumed for Practice I that before a crop was present, the cover
management factor was not modified, whereas after the crop was present, a canopy cover
of 30%, a canopy height of 0.5 m and a relative root network factor of 30% were provided;
for Practices II and III, the canopy cover was taken to be 90%, the canopy height was taken
to be <0.5 m and a relative root network factor of 90% was assumed. The resulting values
were:
5-3
-------
Parameter
No. Name
4 BTLAG
5 CN
6 AMC
I
0.2
78
3 (TCROP)
0.32 0.32
4.76 4.76
1.25 1.25
0.45 (TCROP)
0.32
4.76
1.25
0.02
The initial value (0.02) for USLEC in Practices II and III was subsequently shown to
cause errors that halted operation of the program, so in subsequent runs and for all other
sites that parameter value was changed to 0.05.
5.2. SITE 2: CHAVES COUNTY, NM
Values for site-specific variables for Site 2 were chosen to represent an agricultural area
near Roswell, a city in southeast New Mexico.
5.2.1. Description of Soil. The soil chosen for the model run is the Pecos Series, which
comprises fine, mixed, thermic Torrertic Haplustolls. It is further described as follows
(USDA,1980):
The Pecos series are deep, moderately well drained, very slowly permeable soils on
flood plains. The soils formed in calcareous, saline, stratified, clayey alluvium.
Slope is 0 to 1 percent.
Typically, the surface layer is reddish brown silty clay loam about 12 inches thick.
The upper 10 inches of the substratum is reddish brown clay, the next 20 inches is
reddish brown silty clay and silty clay loam, and the lower part to a depth of 60
inches or more is brown loam and fine sandy loam. Salinity is moderate. Available
water capacity is high.
Pecos soils are of hydrologic group D, characterized by a very slow infiltration rate (high
runoff potential) when thoroughly wet. They consist chiefly of clays that have a high shrink-
swell potential, soils that have a permanent high water table, soils that have a claypan or
clay layer at or near the surface, and soils that are shallow over nearly impervious material.
These soils have a very slow rate of water transmission.
5-4
-------
5.2.2. Narrative Climatologic Summary.
The climate at Roswell conforms to the basic trend of the four seasons, but shows
certain deviations related to geography. A location south and west of the main part
of major weather activity affords a degree of climatic seclusion. There are also
topographic effects that are inclined to alter the course of the weather in this area.
Higher landmasses almost surround the valley location, with a long, gradual descent
from points southwest through west and north. The topography acts to modify air
masses, especially the cold outbreaks in wintertime. Downslope warming of air, as
well as air interchange within a tempering environment, often prevents sharp
cooling. Moreover, the elevation of 3,600 feet in common with the geographic
situation, discourages a significant part of the heat and humidity that originates in
the south in summer. In winter, subfreezing at night is tempered by .considerable
warming during the day. Zero [°f] or lower temperatures occur as a rule a time
or two each winter. Subzero cold spells are of short duration. Winter is the season
of least precipitation (NOAA, 1981).
5.2.3. Temperature. The monthly average temperatures at this location ranged between a
low of 4.2° C and a high of 26.2° C.
:«'
5.2*4. Rainfall. Times, duration and total rainfall for each rainfall event were constructed
from the rainfall record for 1980 at the location (NOAA, 1981). The record provided the
date and amount of the largest rainfall during a 24-hour period each month, as well as the
total amount of rainfall each month. The largest rainfall (greater than the subroutine's
lower limit of 1 cm) was always used, and the remaining rainfall during the period was
divided into events placed at arbitrary times. The storm advancement coefficient was
chosen to reflect the nature of rainfall in the region; the low number used reflects a
preponderance of thunderstorms and sudden showers, whereas larger numbers were used
for some other sites to reflect a more gradual buildup of the rainstorm. The resulting
parameters were as follows:
5-5
-------
Event
No.
1
2
3
4
5
6
7
8
9
10
START
(hr>
328
784
966
1280
1830
2174
2366
2800
3328
3518
PDUR
Chr)
5
8
6
3
8
10
10
5
10
6
PTOT
(cm)
1.17
4.5
4.55
1.5
3.05
7.75
12.47
3.45
4.19
3.0
52.5. Parameters for Subroutine RAINS. Parameters for Subroutine RAINS were
modified to describe local rainfall and soil conditions. The slope value used in the model
run was 1 degree (1.7%). The resulting values were:
Parameter
No. Name
4
5
7
8
9
10
11
BTLAG
CN
STAD
USLEK
USLEL
USLES
USLEC
I
0.32
89
0.25
0.32
2.54
0.16
0.45 (
-------
Fayette soils are of hydrologic group B, characterized by moderately low runoff potential,
moderate infiltration rates, and moderate rates of water transmission.
5.3.2. Narrative Climatologic Summary. Because a meteorologic report for Clinton County
was not included in NOAA (1981), the climatolbgic summary and data reported for nearby
Dubuque, IA, (NOAA, 1981) were used:
The principal feature of the climate in Dubuque is its variety. Standing, as it does,
at the crossroads of the various air masses that cross the continent, the Dubuque
area is subject to weather ranging from that of the cold, dry, arctic air masses in the
winter with readings as low as 32° below [-36°C], when the ground is snow covered,
to the hot, dry weather of the air masses from the desert southwest in the summer
when the temperatures reach as high as 110° [43°C]. More often the area is
covered by mild Pacific air that has lost considerable moisture in crossing the
mountains far to the west, or by cool, dry Canadian air, or by warm, moist air from
the Gulf regions. Most of the year the latter three types of air masses dominate
Dubuque weather, with the invasions of Gulf air rarely occurring in the winter.
The seasons vary widely from year to year at Dubuque; for example, successive
invasions of cold air from the north may just reach this far one winter and bring a
long, cold winter with snow-covered ground from mid-November until March, and
many days of sub-zero temperatures, while another season the cold air may not
reach quite this far and the.winter can be mild with bare ground most of the season,
and only a few sub-zero readings. The summers, too, may vary from hot and humid
with considerable thunderstorm activity when the Gulf air prevails, to relatively cool,
dry weather when air of northerly origin dominates the season.
5.3.3. Temperature. The monthly average temperatures at this location ranged between a
low of -7.5° C and a high of 23.2° C.
5.3.4. Rainfall. Times, duration and total rainfall for each rainfall event were constructed
from the rainfall record for 1980 (NOAA, 1981). The resulting rainfall parameters were as
follows:
5-7
-------
Event
No.
1
2
3
4
5
6
7
8
9
10
START
flirt
180
231
396
636
970
1264
1791
1834
1934
2080
PDUR
flirt
8
6
8
6
6
8
10
10
6
4
PTOT
(cm)
2.84
1.0
1.6
1.2
1.0
1.27
6.12
4.56
3.0
2.5 .
5.3.5. Parameters for Subroutine RAINS. Parameters for Subroutine RAINS were
modified to describe soil conditions for Clinton County, LA, and rainfall for Dubuque, LA,
the nearest reporting station. The slope value used in the model run was 4.6° (8%). The
resulting values were:
Parameter
No. Name
4
5
6
7
8
9
10
11
BTLAG
CN
AMC
STAD
USLEK
USLEL
USLES
USLEC
I
0.17
78
3 (TCROP)
0.375
0.37
4.76
0.85
0.45 (TCROP)
PRACTICE
II
0.26
61
2
0.375
0.37
4.76
0.85
0.05
NUMBER
III
0.26
61
2
0.375
0.37
4,76
0.85
0.05
5.4. SITE 4: HIGHLANDS COUNTY, FL
Values for site-specific variables for Site 4 were chosen to represent a sandy soil in
central Florida. These soils can be productive for agriculture but can be improved greatly
by amendment.
5-8
-------
5.4.1. Description of Soil. The soil chosen for the model run is the Archbold Series, which
comprises hyperthermic, uncoated Typic Quartzipsomments. It is further described as
follows (USDA, 1989):
The Archbold series consists of nearly level to gently sloping, moderately well
drained, droughty soils that formed in marine and eolian deposits. These soils are
on moderately high ridges in the ridge part of the county. The slopes range from
0 to 5 percent.
»
Typically, the surface layer is gray sand about 4 inches thick. The underlying
material to a depth of 80 inches or more is white sand.
The soil reaction is slightly acid to extremely acid. The texture is sand or fine sand.
The content of silt plus clay in the 10- to 40-inch control section is less than 2
percent.
Archbold soils are of hydrologic group A, characterized by having a high infiltration rate
(low runoff potential) when thoroughly wet. They consist mainly of deep, well drained to
excessively drained sands or gravelly sands. These soils have a high rate of water
transmission.
5.4.2. Narrative Climatologic Summary. Because meteorologic information was not given
in NOAA (1981) for Highlands County, the summary and data for nearby Orlando, FL, were
used.
Orlando, by virtue of its location in the central section of the Florida peninsula
(which is abounding with lakes), is almost surrounded by water and, therefore,
relative humidities remain high here the year round, with values hovering near 90
percent at night and dipping to 40 to 50 percent in the afternoon (sometimes to 20
percent in the winter).
The rainy season extends from June through September (sometimes through
October when tropical storms are near). During this period, scattered afternoon
thundershowers are an almost daily occurrence, and these bring a drop in
temperature to make the climate bearable (although, most summers, temperatures
above 95° [35°C] are rather rare). Too, a breeze is usually present, and this also
contributes towards general comfort.
5.4.3. Temperature. The monthly average temperatures at this location ranged between a
low of 15.8° C and a high of 28.0° C.
5-9
-------
5.4.4. Rainfall. Times, duration and total rainfall for each rainfall event were constructed
from the rainfall record for 1980 at Orlando (NOAA, 1981). The resulting parameters were
as follows:
Event
No.
1
2
3
4
5
6
7
8
9
10
START
flirt
1043
1166
1667
1789
1958
2536
2918
3301
3547
4025
PDU
flirt
7
8
10
9
6
10
6
6
7
10
PTOT
(cm)
2.1
3.12
11.17
5.7
3.0
3.17
2.0
1.6
2.44
9.93
In model runs from Practice I, Subroutine RAINS returned a floating-point error during
computations for rainfall event 9. This error did not occur for Practices II and III, so it was
probably related to both the number of organisms and the long time over which the
subroutine operated. To complete the model run, it was necessary to delete rainfall events
9 and 10 for Practice I.
5.4.5. Parameters for Subroutine RAINS. Parameters for Subroutine RAINS were
modified to describe local rainfall reported for Orlando, FL, and soil conditions for
Highlands County, FL. The slope value used in the model run was 1.2° (2%). The
resulting values were:
Parameter
No. Name
4
5
7
8
9
10
11
BTLAG
CN
STAD
USLEK
USLEL
USLES
USLEC
I
0.45
67
0.2
0.1
2.54
0.26
0.45 (TCROP)
PRACTICE
II
0.8
39
0.2
0.1
2.54
0.26
0.05
NUMBER
III
0.8
39
0.2
0.1
2.54
0.26
0.05
5-10
-------
5.5. SITE 5: KERN COUNTY, CA
Values for site-specific variables for Site 5 were chosen to represent a soil near
Bakersfield, CA, which is located in southern California.
5.5.1. Description of Soil. The soil chosen for the model run is the Arvin series, which
comprises coarse-loamy, mixed, nonacid, thermic Mollic Xerofluvents. It is further described
as follows (USDA, 1981c):
The Arvin series consist of very deep, well drained soils on alluvial fan, stream flood
plains, and stream terraces. These soils formed in mixed alluvium derived from
granitic rock. Slope ranges from 2 to 9 percent.
Clay content ranges from 5 to 18 percent in the control section. Organic matter
content is 0.9 percent or less. Reaction is slightly acid to mildly alkaline throughout.
Arvin soils are of hydrologic group B, characterized by moderately low runoff potential,
moderate infiltration rates and moderate rates of water transmission.
5.5.2. Narrative Climatologic Summary.
Bakersfield, situated in the extreme south end of the great San Joaquin Valley, is
partially surrounded by a horseshoe-shaped rim of mountains with an open side to
the northwest and the crest at an,average distance of 40 miles.
The Sierra Nevadas to the northeast shut out most of the cold air that flows
southward over the continent during winter. They also catch and store snow, which
provides irrigation water for use during the dry months. The Tehachapi Mountains,
forming the southern boundary, act as an obstruction to northwest wind, causing
heavier precipitation on the windward slopes, high wind velocity over the ridges and,
at times, prevailing cloudiness in the south end of the valley after skies have cleared
elsewhere. To the west are the coast ranges, and the ocean shore lies at a distance
of 75 to 100 miles.
Because of the nature of the surrounding topography, there are large climatic
variations within relatively short distances. These zones of variation may be
classified as Valley, Mountain, and Desert areas. The overall climate, however, is
warm and semi-arid. There is only one wet season during the year, as 90 percent
of all precipitation falls from October through April, inclusive. Snow in the valley
is infrequent, with only a trace occurring in about one year out of seven.
Thunderstorms also seldom occur in the valley (NOAA, 1981).
5.5.3. Temperature. The monthly average temperatures at this location ranged between a
low of 8.5° C and a high of 28.8° C.
5-11
-------
5.5.4. Rainfall. Times, duration and total rainfall for each rainfall event were constructed
from the rainfall record for 1980 at the location (NOAA, 1981). The resulting parameters
were as follows:
Event
No.
1
2
3
4
5
6
7
8
9
10
START
fhr1)
16
4378
7256
7530
8016
8320
' 8606
8782
13164
16022
PDUR
(hri
8
10
9
6
6
5
8
8
10
9
PTOT
(cm')
1.0
1.78
1.47
1.07
1.0
1.0
1.63
1.0
1.78
1.47
5.5.5. Parameters for Subroutine RAINS. Parameters for Subroutine RAINS were
modified to describe local rainfall and soil conditions. The slope value used in the model
run was 1.7° (3%). The resulting values were:
Parameter
No. Name
4
5
6
7
8
9
10
11
BTLAG
CN
AMC
STAD
USLEK
USLEL
USLES
USLEC
I
0.3
78
3 (
-------
5.6.1. Description of Soil. The soil chosen for the model run is the Kittitas Series, which
comprises fine-silty, mixed (calcareous), mesic Fluvaquentic Haplaquolls. It is further
described as follows (USDA, 1985):
The Kittitas series consists of very deep, somewhat poorly drained soils on flood
plains. These soils formed in mixed alluvium. Slopes range from 0 to 2 percent.
Kittitas soils are of hydrologic group C, characterized by a slow infiltration rate when
thoroughly wet. They consist chiefly of soils having a layer that impedes the downward
movement of water or soils of moderately fine texture or fine texture. These soils have a
slow rate of water transmission.
5.6.2. Narrative Climatologic Summary.
Yakima is located in a small east-west valley in the upper (northwestern) part of the
irrigated Yakima Valley. Local topography is complex with a number of minor
valleys and ridges giving a local relief of as much as 500 feet. This complex
topography results in marked variations in air drainage, winds, a.nd minimum
temperatures within short distances.
The climate of the Yakima Valley is relatively mild and dry. It has characteristics
of both maritime and continental climates, modified by the Cascade and the Rocky
Mountains, respectively. Summers are dry and rather hot, and winters cool with
only light snowfall. The maritime influence is strongest in winter when the
prevailing westerlies are the strongest and most steady. The Selkirk and Rocky
Mountains in British Columbia and Idaho shield the area from most of the very cold
air masses that sweep down from Canada into the Great Plains and eastern United
States. Sometimes a strong polar high pressure area over western Canada will occur
at the same time that a low pressure area covers the southwestern United States.
On these occasions, the cold arctic air will pour through the passes and down the
river valleys of British Columbia, bringing very cold temperatures to Yakima. That
this happens infrequently is shown by the occurrence of temperatures of 0 degrees
[F] or below on only 4 days a winter on the average. On about 21 days during the
winter the temperature will fail to rise to the freezing point. In January and
February 1950, there were 4 consecutive days colder than -20° [-29°C], including -
25° [-32°C] on February 1. However, over one-half of the winters remain above
0 degrees [F (-18°C)] (NOAA, 1981).
5.6.3. Temperature. The monthly average temperatures at this location ranged between a
low of -1.5° C and a high of 22.3° C.
5-13
-------
5.6.4. Rainfall. Times, duration and total rainfall for each rainfall event were constructed
from the rainfall record for 1980 at the location (NOAA, 1981). The resulting parameters
were:
Event
No.
1
2
3
4
5
6
7
8
9
10
START
fhr)
1628
4290
4506
5490
5722
5966
7002
7212
7498
7816
PDUR
far)
6
8
10
6
6
6
10
8
8
8
PTOT
(cm)
1.0
1.25
2.06
1.14
1.0
1.0
2.65
1.5
1.2
1.0
5.6.5. Parameters for Subroutine RAINS. Parameters for Subroutine RAINS were
modified to describe local rainfall and soil conditions. The slope value used in the model
run was 0.6° (1%). The resulting values were:
Parameter
No. Name
4
5
6
7
8
9
10
11
BTLAG
CN
AMC
STAD
USLEK
USLEL
USLES
USLEC
I
0.4
85
2
0.4
0.43
2.54
0.12
0.45 (TCROP)
PRACTICE
II
0.5
74
2
0.4
0.43
2.54
0.12
0.05
NUMBER
III
0.5
74
0.4:
2.5'
0.1:
0.0!
5-14
-------
6. RESULTS
6.1. SENSITIVITY TO VARIABLES
In a preliminary assessment of this application of the model, ~ 1200 model runs were
made, using the ranges of values listed in Tables 4-1 through 4-9. The site-specific
parameters used were those representing Site 1. As an example of the results, Appendix
B lists the maximum probability of infection for any day in each model run for Site 1,
Practice I. Included in Appendix C are representative summary sheets giving the maximum
and cumulative risk of infection in each exposure compartment, the maximum groundwater
flux, the maximum population in each compartment, and the time that the maxima occurred.
Tables 6-1 and 6-2 also indicate which exposure compartments were significantly affected
in each practice by changes in the input variables. In cases where the risk of infection was
increased, the entries in the table are highlighted in bold-face type.
The results of the model runs clearly indicate that the highest risk of infection occurs
during and immediately after application of the sludge. Die-off and dilution by soil
subsequently reduce the number of infectious organisms very rapidly. In fact, to compare
the results obtained by using different values of the input parameters, it was necessary to
increase the concentration of pathogens in the applied sludge from the typically observed
5xl04/kg to an unrealistically high value of 5xl06/kg. In addition, an extremely conservative
value of infective dose was used (see below). Aside from the expected dependence of
exposure on total numbers of pathogens present (ASCRS [P(l)j and APRATE [P(2)]) and
the infectious dose (MID [P(12)]), the most significant effects on exposure appear to be
related to die-off rates (variables P(35)-P(39) and to dry paniculate aerosol formation
(variables P(26)-P(28)). Parameters SUBSOL [P(44)], which describes fractional transfers
of pathogens from soil to subsoil, and SUSPND [P(45)], which describes suspension of
pathogens from surface soil into water that has not yet infiltrated the soil, are also
significant, as is the volume of the onsite pond in which the contaminated runoff water is
diluted.
6-1
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2
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11
1
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rt Q
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Definition
Parameter
Name
*
in
r-t 1-4
r-l ^ CO
000
"o
V)
silt content <
Fractional
CO
pa
s
r- 1 fSJ IT)
fi3
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t— < i— 1 r-l
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1
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1'
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T— 1
co
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1
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i- PS
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8
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£
^
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Parameter
Name
f5 CO
ui in
ff CO"
<*i K
y
C
' " O
C5 ^ 0 !- S g«®
'^ T-l 7^
£
05
l-i
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*s § |
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•S u ^.-^
1 -5 §e
§ «4-l CO
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3s 1 : N
§§ 1 fl
{S.s .a ..II-.
5 x
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co «* *n
CO CO CO
^ ^
in in
fj^ CO
u
G
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CO .g-^
.ti
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CD
Minimum month]
temperature (° C
H
vo
- CO
CO CO
^i ^
in in
CO CO
°i ^
T-l C
rs] ^T
c4 rr os
o c> o
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Slope of inactivat
temperature curv
SLOPEP
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... co .
6-7
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3
3
00
t5 Q
2
PM
o pj
ll
0
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p
Definition
Parameter
Name
*
o,«S
222
K* ^5
o •*
Intercept of inactivatio
temperature curve, dry
NTRCPP
o
CO CO
0\
O O '""*
o
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§
J_*
«J
Transfer fraction, appli
soil surface
ASLSUR
T-l '
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B
c
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"tZ>
w
Transfer fraction, appl
subsurface soil
FSSUR
j- • •'../•• ;.-. i
^
„,
' v . .
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4-»
1 ''-
<+-4
T3
Transfer fraction, soil i
surf ace, runoff
FRRAIN
• '. '••- ' *• -
^
T-H
CO
JJ*4
O
L^
Transfer fraction, soil >
subsurface soil
SUBSOL
v •••? * •- ':.,:
^
'
T—l
•
333
s
1
H-l
TH • .
a
Transfer fraction, soil
soil surface water
SUSPND
£
:
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^S i^j^
2 ;
1
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s
Transfer fraction, soil
crop 1 ,,
' ' ' '
FCROPl
:-£ :
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to
I
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1-^ T— 1
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surfa
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FCROP2
FCROP3
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FCROP5
FCROP6
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6-9
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6-10
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8
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Name
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'S jS "° -a
C/3 rt-i S-J Cy
2 2 2 |
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6-11
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J
••->
o
S
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^to
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Q [L,
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Definition
,_
ex ^ g^
g .< S
b _?P • jo
,__. xjlx Cy
1 ^ --a
ox! a -
f * o •§- , •
l> oo . ' ed ,
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6-12
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1
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5/5
Pu,
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Parameter
Name
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r-l fj OO TH f5 OO . ,_|
O O O O O O ' '
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O i ®
oo o C
cb ' TJ "^
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a c -B
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C-.J ^J »••< «.
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6-13
-------
3
8.
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S
1-4
P-.
13
>
c
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c
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Q
Parameter
Name
*
ooo cQ ^ ^ °> S °> <=>'N S
T-< f> 00 0- _< (SI C^R^ M ' . .S '
'
•o
1 § '1 : :
^ . rt
V3 ^^
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t/3
1
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u
o
T3
OH
T3
-------
TABLE 6-2
Process Functions and Their Effect on Exposure
Model
Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Crop
P(66)
1
0
-1
1
0
-1
1
0
-1
1
0
-1
1
o "•
-1
Process Function
1
0.001
0.001
0.001
0.005
0.005
0.005
0.02
0.02
0.02
— w
—
—
„
—
—
2
0.0001
0.0001
0.0001
0.001
0.001
0.001
0.01
0.01
0.01
_.
~
«
•>*•
—
~
3
0.001
0.001
0.001
0.001
0.001
0.001
0.01
0.01
0.01
_.
—
—
»i»
—
--
4
..
~
—
„
—
—
..
—
—
0,0002
0.0002
0,0002
0.005
0.005
0.005
Effect on Exposure*
ONSITE
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
1,3,4,5
DR
3
3
3
3
3
3
3
3
3
3
SW
3
3
3
3
3
3
3
3
3
3
"Number indicates the practice in which an effect was observed.
6-15
-------
A comparison of base levels of exposure among practices confirms the previously
observed differences in risk of infection. The risk of ONSITE infection, which occurs as a
result of inhalation and subsequent ingestion of dust generated by incorporation of sludge
or tilling the soil, or by direct contact with infected soil (U.S. EPA, 1989a), was calculated
to be significantly lower in Practice V than in Practices I and IV. The baseline ONSITE
risk for Practices II and III was extremely low (Table 6-3).
In all model runs for Site 1, the maximum probability of infection for OFFSITE and
DRINKER was calculated as < 10'15. For the subsurface injection option, the probability
of infection was calculated as zero in all exposure compartments, because with this
application option the bacterial pathogens are assumed to be deposited below any zone in
which exposure could occur.
Because pathogens are transferred gradually to the soil compartment during
incorporation in Practices I through III, the maximum exposure by direct contact is
calculated as occurring when all of the sludge has been incorporated. During incorporation,
the concentration of incorporated pathogens is less than after incorporation has been
completed, so the highest exposure occurs on the day following incorporation. In every
model run for Practice I (which requires 24 hours for the sludge to dry before the field is
tilled), the maximum probability of infection ONSITE occurred on day 3; whereas for
Practices H and III (which do not require the 24-hour wait), the maximum probability of
infection ONSITE occurred on day 2. For Practices IV and V, the maximum probability
occurred on day 1; in these practices, application and incorporation result in more extensive
direct contact than in the agricultural practices. Using default values for the main program
variables, the maximum daily probability of infection ONSITE was 6.83xlO'3 in Practice I
and 3.49xlO-:0 for Practices II and III. Using the same value of 5xl06 bacteria/kg for
ASCRS in Practices IV and V yielded maximum probabilities of infection of 2.36xlO'3 and
2.57X10"4 respectively. However, the proposed requirements for D&M sludge allow a
maximum of 100 fecal coliforms or fecal streptococci per g volatile sludge solids (U.S. EPA,
1989b). This level would correspond approximately to ASCRS = IxlO5, which in Practice
IV led to a maximum probability of infection of < 10"15.
6-16
-------
TABLE 6-3
Maximum Probability of Infection by Site and Practice
SITE
1
2
3
4
5
6
PRACTICE
I
II
III
IV
V
I
II
III
IV
V
I
II
III
IV
V
I
II
III
IV
'V
I
II
III
IV
V
I
II
III
IV
V
ONSITE
6.83xlO-3
9.02xl(r10
9.02xlO-10
3.06xlO'3
3.96X10-4
3.58xlO-3
3.49X-10-10
3.49X10'10
2.36xlO'3
2.57X1Q-4
3.58xlO'3
3.49xlQ-10
3.49xlO-10
2.36xlQ-3
2.57X10"1
3.58xlO'3
3.49X10'10
3.49xlO-10
2.36xlO-3
2.57x10^
3.58xlO'3
3.45xl(r10
3.45xlO-10
2.36xlO-3
2.57X10-4 .
3.58xlO-3
3.49xlO-10
3.49xlO-10
2.36xlO'3
2.57X10"4
OFFSITE
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
EATER
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
DRINKER
LllxlO'16
l.llxlO-16
l.llxlO'16
0.0
0.0
0.0
l.llxlO'16
l.llxlO'16
0.0
0.0
o.o
l.llxlO'16
l.llxlO-16
0.0
0.0
0.0
l.llxlO'16
l.llxlO'16
0.0
0.0
0.0
l.llxlO"16
l.llxlO-16
0.0
0.0
0.0
l.llxlO'16
l.llxlO-16
0.0
0.0
SWIMMER
1.40X10"8
2.62xlO'13
2.62X10'13
0.0
0.0
0.0
0.0
0.0
0.0
O.Q
l.llxlO'16
l.llxlO'16
l.llxlO-16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.23xlO'7
1.24xlO'7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6-17
-------
A preliminary sensitivity analysis of the model was carried out (U.S. EPA, 1989a) to
determine the relative sensitivity of model output to variations in input parameters. In that
analysis, the values of selected parameters were varied singly, and the calculated numbers
of organisms in various compartments were compared. For many parameters, there was no
effect on the number of pathogens in the direct,contact compartment or in the onsite pond
(i.e., the model was not sensitive to these parameters). The model was shown to be
sensitive to variations in application rate and size of field, which are related to the number
of organisms applied; method of'application, which determines the surface availability of
pathogens; and rate of inactivation (die-off) of the organisms, which determines the number
of pathogens surviving. In this preliminary risk assessment for bacteria, the sensitivity of
probability of infection (rather than the compartment populations) to variations in input
parameters was analyzed as described in U.S. EPA (1989a). In this methodology, the
change in the input variable of interest is divided by its baseline value (dfl/J-i), and the
resulting change in the output of interest is divided by the baseline result (dC/C). A ratio
is then taken of the quotients, S = (dC/C)/(dB/B). For example, when the value of Variable
2, APRATE, was changed from 2.5X104 to IxlO5, the maximum probability of infection
ONSITE was increased from 6.83xlO'3 to i.OO. Thus dB/6 was 7.5xl04/2.5xl04 and dC/C
was 0.9932/0.00683. The sensitivity coefficient S was 145.5/3 = 48.5. This result is in
marked contrast to that observed in the case of. parasites (U.S.EPA, 1990), in which the
sensitivity coefficient of APRATE for infection was nearly 1. The difference is due to the
use of an assumed infective dose of 10,for bacteria in contrast to an infective dose of 1 for
parasites. In some cases, a decrease in the value of the input parameter resulted in an
increase in probability of infection. For example, reducing the volurne of the .onsite pond
(VOLPND) from 100 m3/ha to 20 m3/ha dramatically increased the calculated probability
- , ..,.': C- ' '-••,'•'"'":•
of infection to the swimmer. Thus, the sensitivity coefficient was calculated to be -7.4X105,
the negative value indicating a change in probability in the opposite direction from the
change in the input parameter. In'most cases,'the sensitivity coefficient was .6'JOT,the
relevant exposure "compartments;" that is, there was no effect of changing the value of the
variable. Table 6-4 summarizes the results when there was a response to a change in the
value of a variable in Practice I, using site-specific data for Site 1. In this case, the baseline
-------
TABLE 6-4
Sensitivity Coefficients of Site-Specific Parameters
Parameter
Baseline
1 ASCRS
1 ASCRS
2APRATE
2APRATE
7 AREA
7 AREA
12 MID
12 MID
28EHT
28EHT
44 SUBSOL
44 SUBSOL
45 SUSPND
45 SUSPND
VOLPND
VOLPND
Value
IxlO2
5xl06
2X103
IxlO5
1
400
1
100
1
5
lxlO'5
IxlO-1
lxlO'5
IxlO'1
20
1000
Probability
ONSITE
6.83xlO'3
1.1 lxlO'16
1.1 lxlO'16
1.12X10-8
1.00
9.99xlO'16
S.OOxlQ-3
9.80X10'1
2.22xlO'16
l.OSxlO'2
5.05xlO-3
6.83xlO"3
5.45xlO'3
6.83xlO'3
6.83xlO-3
6.83xlO'3
6.83xlO-3
of Infection
SWIMMER
1.40X10-8
1.1 lxlO'16
l.llxlO^16
2.55xlO-15
2.81X10'1
0
i.iixio-16
5.50X10'1
2.22xlO'16
1.40x10'*
1.40X10-8
1.41X10-8
S.OlxlO'9
1.45X10-8
6.74xlO-9
S.OOxlO-3
2.22xlO'16
Sensitivity
ONSITE
'
--
1.09
48.5
1.11
-0.062
-158
-1.16
-0.174
-1.8xlO'3
-l.OxlO"3
0
0
0
0
Coefficient
SWIMMER
- —
«
1.09
6.7xl06
1.11
-4.37xl07
0
0
-8.9xlO'3
-3.2xlO'3
-3.6xlO'2
-5.2xlO'3
-7.4X105
6-19
-------
concentration of pathogenic bacteria in the applied sludge was taken to be 5xl06/kg, an
exaggerated value required to achieve a sufficient exposure for comparison. The sensitivity
coefficient was not evaluated when the probability of infection was l.llxlO'16 or 2.22xlO~16,
values that are too low to be significant.
These results indicate the great sensitivity of the model to changes in exposure to
potentially infective bacteria. The sensitivity to infective dose is particularly significant. The
number of pathogenic bacteria required to cause infection in an individual is highly variable,
depending on many environmental conditions related to the species and strain of bacteria,
including its intrinsic virulence and any modification of that virulence by its recent history,
as well as the effects of biologic, physical and chemical conditions in the exposure milieu
and variations in sensitivity and exposure activities of the human receptor. For simplicity,
the model employs a Poisson distribution calculation to determine the probability of
infection. In addition, the infective dose chosen for the model runs (MID [P(12)] = 10) is
extremely conservative for most pathogenic bacteria. The sensitivity of the model to
infective dose is demonstrated by the results shown in Figure 6-1. In these model runs,
ASCRS = IxlO6 and the drinking water well was modeled at 20 m from the source in order
to observe an effect. It is clear from this figure that at an infective dose >20, the model
calculates that there is very little risk of infection from pathogens in the most sensitive
exposure compartments. The interaction of infectious dose with concentration of pathogens
in the applied sludge is illustrated in Figures 6-2 through 6-4, which show similar model runs
in which both of these parameters were varied. In the case of onsite exposure (Figure 6-2)
the probability of infection at the highest concentration modeled, 5xl06/kg, did not decrease
dramatically until the infective dose was > 10. At all concentrations a significant decrease
had occurred at an infective dose of 20; only at the highest concentration was the probability
of infection > 10"15. Drinking from a well very close to the source presented no significant
risk at MID >3 (Figure 6-3), whereas the probability of infection by exposure in the onsite
pond was intermediate (Figure 6-4). These results also demonstrate the significance of die-
off rates: at an infectious dose of 10, for example, a 10-fold decrease in bacterial
concentration (representing die-off at a given exponential rate from 5xl06/kg) resulted in
a decrease in probability of infection of ~5xl07, whereas a 100-fold decrease in concentration
6-20
-------
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6-21
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6-22
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6-24
-------
(representing die-off at twice that exponential rate) resulted in a decrease in probabilty of
infection of ~1.3xl015.
Shigella is unusually virulent but present at low concentrations in treated sludge. To
model risk from Shigella, ASCRS was assumed to be 100, the MID = 1, and other parameters
were the same as for Figures 6-1 through 6-4. The results indicated a maximum probability
of infection ONSITE of LSSxlO"4 and to the SWIMMER, 2.99xlO'5.
Overall, the sensitivity analyses of Practices II and HI gave results similar to those
for Practice I. Practices IV and V do not include an onsite pond, so there are no
SWIMMER exposures in these compartments. As with the other practices, there were large
variations in S for ONSITE exposures as input variables were changed.
Variables in Subroutine RAINS were also tested. In this test, a single rainfall event
was modeled for simplicity. The largest rainfall used for Site 1, 12.47 cm total rainfall in
10 hr, was arbitrarily placed at Hour 60. Other variables for the base rainfall were BTLAG
= 0.7, CN = 65, AMC = 2, STAD = 0.4, USLEK = 0.32, USLEL = 3.65, USLES = 0.7,
USLEC = 0.25 AND USLEP = 1.0. Because a number of the variables (BTLAG, CN,
USLEL and USLES) are interdependent, their minimum and maximum values were
combined into two model runs. Other parameters were varied independently, as indicated
in Table 6-5. None of the changes in parameters for Subroutine RAINS had an effect on
the ONSITE, OFFSITE, EATER or DRINKER exposures. Effects on SWIMMER were
variable. Specifying a good soil and a 1% slope (model run 1) significantly reduced
transport to the onsite pond, whereas a steeper slope (run 3) caused a dramatic increase in
the probability of infection. Specifying a moist soil (run 5) or poor ground cover (runll)
markedly increased SWIMMER exposure. However, increasing the resuspension factor
SUSPND [P(45)j, which describes the transfer of pathogens from soil to runoff water, only
slightly increased SWIMMER exposure. Therefore, increasing the total surface runoff and
sediment transport increased the SWIMMER exposure more than did increasing surface
runoff alone. As also reported for parasites in sludge (U.S. EPA, 1990), these results imply
that sediment transport is more important than runoff water as a transfer route to the pond.
6-25
-------
TABLE 6-5
Sensitivity of Subroutine RAINS to Input Parameters
Model
Run
1
2
3
4
2
5
6
2
7
8
2
9
10
2
11
12
2
13
Name
BTLAG
CN
USLEL
USLES
BTLAG
CN
USLEL
USLES
BTLAG
CN
USLEL
USLES
AMC
AMC
AMC
STAD
STAD
STAD
USLEK
USLEK
USLEK
USLEC
USLEC
USLEC
SUSPND
SUSPND
SUSPND
Parameter
Value
1.2
40
2.54
0.12
0.7
65
3.65
0.7
0.2
90
4.76
0.7
1
2
3
0.2
0.4
0.5
0.1
0.32
0.5
0.05
0.25
0.5
0
0.001
0.1
Probability
ONSITE
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
of Infection
SWIMMER
9.99E-15
5.39E-03
l.OOE+00
3.08E-10
5.39E-03
2.65E-01
5.82E-03
5.39E-03
4.84E-03
1.51E-03
5.39E-03
9.16E-03
1.19E-03
5.39E-03
2.18E-02
5.83E-03
5.39E-03
1.08E-03
6-26
-------
6.2. EXPOSURE COMPARTMENTS
As reported in Table 6-3, the maximum probability of infection was highest in the
ONSITE exposure compartment in each of the model runs. In these model runs, site-
specific values were used for Subroutine RAINS (Chapter 5), .and default values were used
for the main program variables, with the, exception of FCROP.[P(46)]=5xlO'6, TCROP
[P(68)]=240 and THARV [P(69)]=300. Table 6-3 shows that ONSITE exposures related
to the grazing and feed crop applications are lower than, those for field crops, whereas
exposures for residential applications are similar to those for agricultural applications. The
effects of site-specific variables on exposure are also demonstrated in Table 6-3. The largest
effect was on the SWIMMER exposure because the. variation in timing of rainfalls had a
major effect on the number of remaining viable organisms and on the amount of surface
runoff and sediment transport moving bacteria into the onsite pond., Rainfall times also had
an effect on contamination of the crop surface, resulting in slight variations in the ONSITE
exposures.
6.2.1., ONSITE. The maximum probability of infection ONSITE Was essentially the same
for each practice at all sites, because site-specific variables had little time to act on the
number of organisms in the first three days of the model run. However, site-specific
differences were noted: the maximum probability of infection was calculated to be higher
at Site 1 than at the other sites: The maximum probability of infection at each site, using
default values for SUSPND and die-off rates and a bacterial density of ASCRS=5xl06, was:
Practice I, 0.007; Practices II and III, 9;OxlO-10; Practice IV, 0.0024; and Practice V, 0.0004.
6.2.2. OFFSITE., No infections from sludge pathogens in offsite aerosols were calculated
to have occurred in any of the model runs. The maximum concentration of bacteria in the
offsite compartment was too low in each case to provide a significant exposure. The
shortest distance from source to receptor in the test runs was lOftm. This value was shown
previously to be near the lower .distance limit for the Gaussian-plume algorithm (U.S. EPA,
1989a). Therefore, the model runs imply that droplet aerosols are not likely to be a
'*(";., • i • _" • .'",} " .. .-/; ,'.'.! '.'..•'.','••.*.•„• 'i i
significant source of offsite infection. This conclusion is consistent with the expected low
efficiency of aerosol generation-from materials with1 high solids concentrations.
,: .'' '••. '• ::'•- 1 ?,^i.;-. 1':.'-:-.if *' ',
• 6-27
-------
62.3. Food Consumer (EATER). The number of bacterial pathogens surviving until a crop
was harvested was so small that no risk of infection by eating the crops was observed in any
practice at any site. The model does not allow for colonization of the crops and subsequent
growth of the bacteria; if damaged or rotten fruit or vegetables are colonized by pathogenic
bacteria such as Salmonella, infection could occur as a result of their consumption.
62.4. Groundwater Drinker (DRINKER). Model results did not support a significant risk
of infection by consumption of water from groundwater wells 50 m from the site of
application. Very low probabilities of infection resulted even when well placement was 20
m from the source and the infective dose was unrealistically low (Figures 6-1 through 6-4).
The model is limited in its treatment of subsurface transport. It is based on flow
through a uniform medium whose properties are initially specified and do not change with
time or distance from the source. In contrast, soils contain many cracks and solution
channels, and soil structure may vary significantly over short distances in an aquifer.
Therefore, it is possible for transport of pathogens in groundwater to occur at a site but not
to be predicted by the model. The model also has a limitation in its treatment of transport
from surface soil to groundwater, because there is no subroutine for transport in the
unsaturated zone. To model the results of contamination of the aquifer in ways that are not
predicted by the model, the initial concentration of pathogens in groundwater was set at
IxlO6 and velocity of groundwater was set at 10.8 cm/hr, the other parameters being held
at the standard reference levels.
Risk of infection via groundwater exists if the well becomes contaminated by surface
water. However, it is specified in the modeling assumptions (U.S. EPA, 1989a) that offsite
runoff of surface water is prevented. Use of an onsite well for a drinking water source
would probably constitute a significant risk to humans and should be discouraged as a
management practice. In addition, once bacteria reach a well, it is possible for them to
colonize the screen or packing, especially if there are also organic nutrients in the
groundwater, as would be likely after application of sewage sludge. This provides a further
risk of infection to the groundwater drinker that is not predicted by the model.
6.2.5. SWIMMER. Unlike the results obtained modeling the risk from parasites in sludge
(U.S. EPA, 1990), the probability of infection for SWIMMER in the onsite pond was lower
6-28
-------
than for direct contact ONSITE. The die-off rate for bacteria is sufficiently high that
significant numbers of pathogens were not available to be transported to the pond when the
modeled rainfall events occurred. It is expected, however, that a rain occurring immediately
after application of sludge would present a greater risk. The results at Site 5 confirm this
expectation. At Site 5, the first rainfall occurred at 16 hours. In Practice II and HI, this
resulted in a risk to SWIMMER higher than the risk ONSITE. The SWIMMER risk was
calculated as zero in Practice I because the program did not allow for rainfall before the
sludge was incorporated (24 hours). When the first rainfall was changed to 26 hours, the
maximum SWIMMER risk increased to LSTxlO"4, > 10,000 times higher than at Site 1,
where the first rainfall occurred at 77 hours. When the first rainfall occurred at 180 hours
or later, the SWIMMER risk was insignificant.
6-29
-------
-------
7. CONCLUSIONS
The sensitivity analyses described in this report imply that many parameters of the
model are not of major importance to the calculated risk of infection, either because the
numbers of pathogens in relevant exposure compartments are so low that even orders of
magnitude have little effect, or because the effects occur after the time of maximal exposure.
The risk of infection from bacterial pathogens in treated sewage sludge appears to be small
when judged by model results, but there are a number of factors the model does not
address, including regrowth in composted or D&M sludge. In addition, before the model
can be used to model health risk at any site, default data should be revised on the basis of
literature findings; transfer factors should be revised in light of findings of the test runs;
more data should be provided on pathogen abundance, fate and transport; a subroutine for
transport in the unsaturated zone should be added; more data on infectious doses of
pathogens of concern should be provided; and field verification of predictions of the model
should be done. Therefore, the results presented in this report should not be used to
conclude that sludge application is or is not safe for any application described. The
conclusions below are based on operation of the model and are true only insofar as the
model correctly describes the fate and transport of sludge pathogens.
The model runs indicate that significant exposures are likely only if the number of
organisms is very high, either because the concentration of bacteria in the treated sludge is
unrealistically high or because a high application rate is used. In comparison, the risks from
parasites appear to be much higher (U.S. EPA, 1990). The model results for bacteria are
supported by epidemiologic studies and field experience as described in Chapter 3. Reports
of infection by consumption of sewage-contaminated crops are largely anecdotal. Typically,
if infection by enteric pathogens is reported, raw sewage was used to fertilize the crops.
However, compost and D&M sludge products designed for use in the home garden can
allow multiplication or regrowth of bacteria, such as Salmonella, resulting in extremely
variable pathogen densities and the possibility of a higher dose of pathogens upon ingestion
of crops. Therefore, composted and D&M sludge products should be used with caution.
7-1
-------
7.1. EXPOSURE FACTORS
Significant exposures occurred only in the ONSITE and SWIMMER compartments.
Exposure by direct contact immediately after sludge application could be a source of
infection. However, the risk of infection decreased very rapidly, so that the cumulative risk
of infection from a single application was typically only slightly higher than the maximum
daily risk. The most significant factors in risk of infection appear to be those that relate to
die-off rates. At typical die-off rates, the pathogens quickly decreased to a number too low
to cause infection.
7.2. INFECTIVE DOSE
Another significant factor in determining the risk of infection is the number of
organisms required to establish an infection. As discussed in Section 6.1, the effect of
infective dose on infection probability was determined, using a high concentration of
pathogens in the applied sludge. The results suggest that if the infective dose is >20, the
probability of infection becomes minimal. The reported infective dose for organisms that
appear in sludge in reasonably high concentrations is in the range of lOMO6. More virulent
organisms, e.g., Shigella, have not been reported at significant concentrations in sludge.
Therefore, although they are more likely to cause an infection if there is an exposure, their
number is so low that the risk remains small. In contrast to bacteria, it is expected that the
risk of infection by viruses and by parasites in the treated sludge would be significantly
higher, because it must be assumed that their infective dose is 1.
7.3. SUBSURFACE TRANSPORT
The calculated exposures to groundwater were insignificant when the transfer factors
listed in Chapter 4 were used. The concentration of pathogens in treated sludge is
sufficiently low that the calculated dilution by soil and estimated fractional transfers of
pathogens from soil surface to groundwater made the modeled concentration of pathogens
in groundwater very low. In addition, the time required for transport allowed dispersion and
die-off to reduce the concentrations still further. However, results of the subsurface
transport model, Subroutine GRDWTR, should not be taken as an accurate description of
7-2
-------
subsurface transport. The groundwater model is not realistic, in that it assumes that the soil
between the source and the receptor is homogeneous, with no cracks or channels and no
gradation of structure over the distance the pathogens must travel. This assumption is likely
to cause an underestimation of the number of pathogens reaching the receptor. On the
other hand, the model assumes the entire contribution of one hectare to the groundwater
occurs as a point source. This assumption greatly overestimates the concentration of
pathogens reaching the receptor, while at the same time reducing the time required for a
slug of pathogens to pass the receptor groundwater well.
To test the effect of rapid passage of sludge pathogens directly to groundwater, as in
the case of a very shallow water table and rapid percolation without retardation of the
bacteria by soil, test runs were done in which the transfer factors in soil were increased. In
these tests, SUBSOL [fraction of pathogens transferred from surface to subsurface soil,
P(44)] = 0.99 and FRGRND [fraction of pathogens transferred from subsurface soil to
groundwater, P(53)] = 1. The accuracy of these model runs is limited by the constraints
described above, but they can serve qualitatively to indicate the potential for exposure. The
results, using other parameter values for Site 1, Practice I, and an initial pathogen
concentration [ASCRS, P(l)] of IxlO6, predicted a probability of infection to the
groundwater drinker > IxlO"5 at day 20 with a peak at day 21; the maximum probability of
infection was 0.57, corresponding to a pathogen concentration in groundwater of 0.05/L.
This probability fell below IxlO"5 by day 27. When the default initial concentration of
pathogens (Ixl04/kg) was used, the maximum probability of infection was calculated as
-------
(Burton et al., 1987). In addition, any delay in rainfall after sludge application will tend to
reduce the number of viable pathogens transported to the pond. Major problems with
runoff of potentially pathogenic bacteria from feedlots and similar sources are typically
linked with ongoing production of raw waste rather than with application of treated sewage
sludge.
Runoff and sediment transport were significantly less important when bacteria were
modeled as the pathogens of concern than was observed with parasites. In the latter case,
the onsite pond was the most significant exposure point. This difference reflects both the
persistence of parasite infective forms and the low number required to establish an infection.
7.5. OFFSITE AEROSOLS
No health risk was demonstrated from exposure to offsite aerosols. This result is in
agreement with epidemiologic studies summarized in Chapter 3, in which little health risk
could be associated with living in close proximity to a facility that was known to generate
aerosol releases of potentially pathogenic microorganisms.
7.6. WAITING PERIOD
The results described above do not support the requirement for an extended waiting
period before use of sludge-amended soils, except in the case of composted sludge products
in which pathogens have regrown. In the model runs, bacterial concentrations in all of the
exposure media decreased so rapidly that a waiting period of at most a few days after
incorporation of sludge products appeared to be sufficiently protective for planting, grazing
cattle, or other agricultural activities. Because it is not practical to impose a waiting period
for home use of sludge, users of D&M sludge must be protected during yard and gardening
activities by prevention of high initial concentrations of pathogens. This can be
accomplished by regulations such as those requiring that D&M sludge products contain a
sufficient non-pathogenic microbial flora to prevent significant regrowth of pathogens (U.S.
EPA, 1989b).
7-4
-------
8. RESEARCH NEEDS
8.1. INFORMATION NEEDS FOR BACTERIA
Although much significant research has been done in the past ten years to fill the data
gaps with respect to health effects from land application of sewage sludge, particularly in
relation to bacteria, major information needs still exist. Much of the information on
bacterial densities in sludge is limited to Salmonella or indicator organisms. Furthermore,
' ' " i ' '
accuracy of the available data suffers from lack of reliable, standardized methods for
quantifying bacteria not only in sludge but in all environmental media.
Despite the growing understanding of infective dose as a relationship between bacteria
and the human receptor or host, there is only limited information on low-dose effects on
sensitive individuals. The widespread prevalence of gastroenteritis and the often mild, or
at least non-life-threatening nature of enteric bacterial disease in most adults leads to under-
reporting and a consequent lack of epidemiologic investigation.
Recently there has been significant progress made in understanding how some bacteria
infiltrate and flow through soils, particularly in the saturated zone. Transport data on all
the bacteria of major concern are still needed, especially in surface water and aerosols. In
addition, there is a need for correlation of infection and disease with release of bacteria-
laden aerosols.
In summary, the following information is needed to improve the usefulness of the
Pathogen Risk Assessment Model and to allow for more reliable risk assessment of land
application of sewage sludge:
: .: - ••; '. '« '• .' .-. ;".:\:ii, , •:.,- , • •' : .v - , .: -',• , • . .,, .'.'-..
• Simple and accurate standardized methods for quantifying, by species and strain,
pathogenic bacteria in treated sludge destined for land application, in final D&M
sludge products, and in environmental media;
• Improved understanding of minimum infective doses, particularly low-dose effects
and MIDs for sensitive subjects;
• Additional information on regrowth of bacteria in compost and D&M sludge,
including factors enhancing or limiting regrowth;
8-1
-------
• More accurate survival and transport data on all pathogenic bacteria of major
concern in sludge, especially retardation coefficients for transport of bacteria in
saturated soil;
• Development of an index of soil types that would correlate capacity for solute
transport and suitability for sludge application (also valuable for onsite waste
disposal or solid waste disposal);
• Research on subsurface injection of sludge and the relative probability of bacterial
transport in groundwater; and
• Epidemiologic studies evaluating whether there is a correlation between bacterial
infections (not necessarily disease) and bacterial aerosols. •>••
8.2. MODEL DEVELOPMENT
In the present version, the model does not allow for direct contact with sludge during
incorporation in Practices I, II and III. Instead, it is assumed that all exposures during
incorporation occur via inhalation of dry aerosols generated by the machinery used to
incorporate the sludge. It might be more reasonable to include inadvertent ingestion of
unincorporated sludge in these practices, as occurs in Practices IV and V. In addition, the
model does not consider pathogens in runoff of unincorporated sludge resulting from rainfall
before 24 hours have elapsed. It should be changed to include this potentially significant
exposure pathway.
The model assumes that tilling will not disturb the soil below 15 cm, so bacteria
injected into subsurface soil are not transferred to surface soil. However, plowing is deeper
than tilling and would be expected to redistribute subsurface soil. Some transfer factor
could be added to the model to allow for redistribution of pathogens from subsurface to
surface soil when the field is plowed.
The present version of the Pathogen Risk Assessment Model does not include an
onsite pond in Practice V, the home lawn application. For a better description of sludge
use on public parks and golf courses, which are more likely to have ponds, it might be
beneficial to add the option for existence of a pond onsite. For even more flexibility, the
user could be asked to specify which exposure calculations to print in the output table.
8-2
-------
• , The limits of Subroutine RAINS should be further characterized to establish operating
boundaries for input variables. These boundaries .could be set by having the program return
a warning message and possibly revise the input data to a value that would not cause the
program to crash. .
Limitations in offsite transport subroutines may limit accuracy of the model, but
constraints on the size of a model able to run on a personal computer make it unlikely that
more sophisticated routines can be added. For example, sophisticated air dispersion models
are large and complex and could probably not be added to the current model. Adding the
- capacity to model more than one windstorm (the current model limit) would .probably be
feasible without making the model too unwieldy. ; •
Modifications to make the model more user-friendly are also planned. Developing a
pre-processor to edit the input parameter file would reduce the .size of the main code by
eliminating the input data subroutine, A post-processor would enable the user to display
graphically the results of the model runs, rather than analyzing only number sets as is
currently the case. :
:•. > It is likely that further analysis of the model using viruses as the pathogens of interest
will reveal additional areas :in which the model can be improved.
8-3
-------
-------
9. REFERENCES
Adams, A.P. and J.C. Spendlove. 1970. Coliform aerosols emitted by sewage treatment
plants. Science 169: 1218-1220.
Alexander, M, R.J. Wagenet, P.C. Baveye, J.T. Gannon, U. Mingelgrin and Y. Tan. 1991.
Movement of Bacteria Through Soil and Aquifer Sand. Robert S. Kerr Environmental
Research Laboratory, Office of Research and Development, U.S. EPA, Ada, OK.
EPA/600/2-91/010.
Al-Ghazali, M.R. and S.K. Al-Azawi. 1988a. Effects of sewage treatment on the removal
of Listeria monocytogenes. J. Appl Bacteriol. 65(3): 203-208.
Al-Ghazali, M.R. and S.K. Al-Azawi. 1988b. Storage effects of sewage sludge cake on the
survival of Listeria monocytogenes, J. Appl. Bacteriol. 65(3): 209-213.
Allen, MJ. and S.M. Morrison. 1973. Bacterial movement through fractured bedrock.
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9-16
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APPENDIX A
MODEL OVERVIEW
A-l
-------
-------
MODEL OVERVIEW
Five sludge management practices, representing land application and D&M
management options, are included in the present model and are numbered I-V. They are
listed in Table A-l and illustrated in Figures A-l through A-5. Two of the practices use
heat-dried or composted sludge for residential purposes and three use liquid sludge for
commercial farming operations. Since each of these two types of sludge represents a wide
range of sludge treatment possibilities, the extent of treatment or conditioning prior to land
application must be approximated for each case (i.e., the pathogen concentration in the
applied sludge must be specified). The computer model represents the compartments and
transfers among compartments of the five management practices. The compartments are
the various locations, states or activities in which sludge or sludge-associated pathogens exist;
they vary to some extent among practices. In each compartment, pathogens either increase,
decrease or remain the same in number with time, as specified by "process functions"
(growth, dieoff or no population changes) and "transfer functions" (movement between
compartments). The population in each compartment, therefore, generally varies with time
and is determined by a combination of initial pathogen input, "transfer functions" and
"process functions." The populations of pathogens in the compartments representing human
exposure locations (designated with an asterisk in Figures A-l through A-5 and in Table A-
2), together with appropriate intake and infective dose data, are used to estimate human
health risk.
Although each practice listed in Table A-l is different, all five practices share
common characteristics. All compartments that appear in one or more of the five sludge
management practices are listed in Table A-2. Those compartments with an asterisk
represent exposure sites for the human receptor:
• 3* inhalation or ingestion of emissions from application of sludge or
tilling of sludge/soil;
• 5* inhalation or ingestion of windblown or mechanically generated
particulates;
• 6* swimming in a pond fed by surface water runoff;
A-3
-------
TABLE A-l
Sludge Management Practices and Descriptions in
Pathogen Risk Assessment Model
PRACTICE
DESCRIPTION13
II
HI
IV
V
Application of Liquid Treated Sludge for Production of
Commercial Crops for Human Consumption
Application of Liquid Treated Sludge to Grazed Pastures
Application of Liquid Treated Sludge for Production of
Crops Processed before Animal Consumption
Application of Dried or Composted Sludge to Residential
Vegetable Gardens
Application of Dried or Composted Sludge to Residential
Lawns
"Source: U.S. EPA, 1989a
bTwo types of sludge are used in this model - liquid and dried/composted. The extent
of treatment or conditioning prior to application is variable and must be determined
for each case.
A-4
-------
Subsurfaca
Soil
Groundwater
Offsita
Well
J_ 1
Application
Incorporation
Soil
Surfaca
Crop
Surfaca
Harvesting
v 16
Commercial
Crop
Irrigation
Water
Appilcation/TIHIng
Emissions
External.
Source
'" I '..I*.
FIGURE A-1
Input/Output Diagram for Practice I - Application of Liquid Sludge
for Production of Commercial Crops for Human Consumption
,A-5
-------
3*
Application/Tilling
Emissions
External
Sourca
FIGURE A-2
Input/Output Diagram for Practice I! - Application of Liquid
Sludge to Grazed Pastures
A-6
-------
Application/Tilling
Emissions
External
Sourca
FIGURE A-3
Input/Output Diagram for Practice III - Application of Liquid Sludge
for Production of Crops Processed before Animal Consumption
A-7
-------
Subsurface
Soli
J_s
Application
Incorporation
Soil
Surfaca
Crop
Surface
15
Harvesting
16*
Crop
Application/Tilling
Emissions
FIGURE A-4
Input/Output Diagram for Practice IV - Application of Dried
or Composted Sludge to Residential Vegetable Gardens
A-8
-------
Subsurfaca
Sol!
Application/Tilling
Emissions &.
FIGURE A-5
Input/Output Diagram for Practice V - Application of Dried
or Composted Sludge to Residential Lawns
A-9
-------
TABLE A-2
Compartments Included in the Sludge Management Practices
Compartment
Name and Number
Application
ncorporation
Application/Tilling
Emissions
Soil Surface
Particulates
Surface Runoff
Direct Contact
Subsurface Soil
Groundwater
Irrigation Water
Soil Surface Water
Offsite Well
Aerosols
Crop Surface
Harvesting
(Commercial) Crop
Animal Consumption
Meat
Manure
Milk
Hide
Udder
aSource: U.S. EPA, 1989a
bAsterisk indicates exposure
Liquid Sludge Dried/Composted
Management Practices Sludge Management
Practices
1
1
2
2*b
4
5*
6*
7*
8
9
10
11
12*
13*
14
15
16*
compartments.
II
1
2
3*
4
5*
6*
7*
8
9
10
11
12*
13*
14
• 17
18*
19
20*
21
22
III IV
1 1
2
3* 3*
4 4
5* 5*
6*
7* 7*
8 8
9
10
11 11
12*
13*
14 14
15 15
16*
17
18*
19
20*
21
22
V
1
3*
4
5*
7*
8
11
14
A-10
-------
• 7* direct contact with sludge-contaminated soil or crops (including
grass, vegetables, or forage crops),;
• 12* drinking water from an offsite well; . .
• 13* inhalation and subsequent ingestion of aerosols from irrigation;
16* consumption of vegetables grown in sludge-amended soil;
• 18* consumption of meat or
• 20* consumption of milk from cattle grazing on or consuming forage
from sludge-amended fields.
The first 14 compartments, most of which are common to all practices, are described
below.
APPLICATION (1) represents the application of sludge to a field (default size 10 ha)
or to a yard or garden of specified size. Liquid sludge may be applied by spread-flow
techniques, by spray, or by subsurface injection. The application rate and pathogen
concentrations are variables to be entered by the user of the model. During spread-flow
and spray application, sludge will be spread thinly on the soil, where it will be subject to
drying, heating and solar radiation, thus losing the protective benefits provided by bulk
sludge. It is assumed, therefore, that inactivation will occur at a rate characteristic of the
organism in soil at 5°C above the ambient temperature (Brady, 1974; USDA, 1975). It is
also assumed that liquid sludge is absorbed by the upper 5 cm of soil surface during this
time. The default time period for transfer from APPLICATION (1) to INCORPORATION
(2) is 24 hours, which allows a field treated with liquid sludge to dry sufficiently to plow or
cultivate. If the injection option is chosen, the liquid sludge goes directly to SUBSURFACE
SOIL (8) at hour 10. During spray application of liquid sludge or application of dry
composted sludge, droplets or loose particulates may become airborne. Liquid aerosols are
modeled by a Gaussian-plume air dispersion model that calculates the downwind
concentration of airborne particulates. Dry particulate emissions are calculated using
models for generation of dust by tilling or mechanical disturbance of soil. Both are
represented as transfers from APPLICATION (1) to APPLICATION/TILLING
EMISSIONS (3).
A-ll
-------
INCORPORATION (2) involves the mixing, by plowing or cultivation, of the sludge
and sludge-associated pathogens evenly throughout the upper 15 cm of soil. Process
functions associated with this compartment are the same as for the relevant pathogen type
in soil. Particulate emissions generated by cultivation are represented by a transfer from
INCORPORATION (2) to APPLICATION/TILLING EMISSIONS (3) beginning at hour
24, extending for enough time to cultivate the field (at a rate of 5 ha/hr) or till the garden
or lawn (at a rate of 0.002 ha/hr). At the end of this time, all remaining pathogens are
transferred to SOIL SURFACE (4).
APPLICATION/TILLING EMISSIONS (3*) is an exposure compartment that
receives the'dust, or suspended particulates, generated by application or by the tilling of
dried sludge or sludge-soil mixture. It also receives aerosols generated by spray application
of liquid sludge. All process functions associated with this compartment are incorporated
in the aerosol subroutines. Exposure in this compartment is by inhalation but, as in all
inhalation exposures, model simplification limits the exposure to the pathogens assumed to
be ingested after the inhaled dust or aerosol spray is trapped in the upper respiratory tract,
swept back to the mouth by ciliary action and swallowed.
SOIL SURFACE (4) describes the processes occurring in the upper 15 cm (Practices
I, IV and V) or upper 5 cm (Practices II and HI) of the soil layer. Microbes are inactivated
at rates characteristic for moist soil at 5°C above the chosen ambient temperature (Crane
and Moore, 1986; Kibbey et al, 1978). Transfers from SOIL SURFACE (4) occur by wind
to WIND-GENERATED PARTICULATES (5), at a time chosen by the user, by surface
runoff and sediment transport after rainfall events to SURFACE RUNOFF (6), by a person
walking through the field or contacting soiled implements or clothing or by other casual
contact to DIRECT CONTACT (7), by leaching after irrigation or rainfall to
SUBSURFACE SOIL (8), by resuspension during irrigation or rainfall to SOIL SURFACE
WATER (11), or at harvest to CROP SURFACE (14).
WIND-GENERATED PARTICULATES (5*) describes the airborne particulates
generated by wind. Process functions are the same as for the organism in air-dried soil at
the ambient temperature (Crane and Moore, 1986; Kibbey et al., 1978). The exposed
individual is standing in the field or at a user-specified distance downwind from the field
A-12
-------
during a windstorm. The wind-generated exposure is calculated from user-specified values
for duration arid severity of the windstorm (default values, 6 hr at 18 m/sec (40 mph)).
SURFACE RUNOFF (6*) is an exposure compartment describing an onsite pond
containing pathogens transferred from SOIL SURFACE (4) by surface runoff and sediment
transport after rainfall. These processes are described by a separate subroutine.
Inactivation rates in this compartment are characteristic of microbes in water and are much
lower than rates for soil. Water is removed from the pond by infiltration and recharge of
the groundwater aquifer, but it is assumed that no microbes are transferred by this process.
The human receptor is an individual who incidentally ingests 0.1 L of contaminated water
while swimming in the pond. This compartment is also an exposure compartment for cattle
drinking 20 L of water daily from the pond (Practice II).
DIRECT CONTACT (7*) is the exposure compartment for a worker or a child less
than 5 years old who plays in or walks through the field, yard or garden, incidentally
ingesting 0.1 g of soil or vegetation at the daily geometric mean concentration of pathogens.
This human receptor represents the worst-case example of an individual contacting
contaminated soil or soiled clothing or implements. No process functions are associated
with this compartment because it is strictly an exposure compartment.
SUBSURFACE SOIL (8) describes the processes and transfers for pathogens in the
subsurface soil between 5 or 15 cm depth and the water table. It also serves as the
incorporation site for subsurface injection of liquid sludge. Process functions in
SUBSURFACE SOIL (8) are the same as for moist soil at the ambient temperature. The
transfer from SOIL SURFACE (4) occurs after each rain or irrigation event as a result of
leaching from the soil surface. The time of transfer is calculated by dividing the depth of
rainfall or irrigation by the infiltration rate. Transfer to GROUNDWATER (9) is
arbitrarily set at one hour later. At present, the relation between unsaturated water flow
and subsurface transport has not been well-established. Thus, this model lacks a satisfactory
subroutine to describe pathogen transport from the subsurface soil to groundwater. Instead,
user-specified variables are used to describe the fraction of pathogens transferred from SOIL
SURFACE (4) to SUBSURFACE SOIL (8) and from SUBSURFACE SOIL to
GROUNDWATER. >i
A-13
-------
GROUND WATER (9) describes the flow of pathogens in the saturated zone.
Process functions are the same as for other water compartments. Transfers occur to
IRRIGATION WATER (10) if the water is needed for irrigation or to OFFSITE WELL
(12*) if the water is used for drinking. The number of pathogens transferred to
IRRIGATION WATER (10) is based on the concentration of pathogens in the groundwater
compartment and the total depth of irrigation. The transfer to OFFSITE WELL (12) is
described by a modification of the subsurface solute transport model of van Genuchten and
Alves (van Genuchten and Alves, 1982). Because microbes in suspension are passively
transported by bulk water flow and interact with soil particles by adsorption and desorption,
they behave similarly enough to dissolved chemicals that existing solute transport models can
be used to describe their fate in the saturated zone (Gerba, 1988).
IRRIGATION WATER (10) describes the transfers for pathogen-contaminated water
used for irrigation. No processes are associated with this compartment because it is
intended as a transition compartment. Irrigation of the field, lawn or garden takes place a
user-specified number of times each week. This irrigation water may come from either an
onsite well fed by GROUNDWATER (9) or from an outside source of treated, liquid
sludge. The default conditions vary by practice. In either case, AEROSOLS (13) are
generated unless a non-spray option is chosen. Spray irrigation is the default since it would
be most likely to cause a significant exposure to workers or offsite persons. In addition to
aerosol emissions, irrigation transfers pathogens to CROP SURFACE (14) and to SOIL
SURFACE WATER (11).
SOIL SURFACE WATER (11) represents any irrigation water or rainfall in contact
with the ground prior to infiltration. This compartment describes the temporary suspension
of pathogens in such a water layer and their subsequent transfer to CROP SURFACE (14)
or to SOIL SURFACE (4). Process functions are the same as for other water
compartments.
OFFSITE WELL (12*) is the exposure site for a human receptor drinking 2 L/day
of contaminated water whose pathogens have been transported through groundwater.
Process functions are the same as for groundwater. The groundwater transport subroutine
supplies the concentration of pathogens in the well at a user-specified distance from the
A-14
-------
source. No transfers out of the compartment are specified because it is an exposure
compartment only.
AEROSOLS (13*) describes fugitive emissions from spray irrigation, which occurs
at a default rate of 0.5 cm/hr for 5 hr. The source of irrigation water producing
AEROSOLS can be an onsite well (i.e., GROUND WATER) or liquid sludge. A Gaussian-
plume model is used to calculate concentrations of airborne microbes downwind. The
human receptor is an onsite worker or a person offsite who is exposed during the time of
irrigation.
CROP SURFACE (14) describes contamination of vegetable or forage crops by
transfer of user-specified amounts to or from SOIL SURFACE (4), from IRRIGATION
WATER (10), or to or from SOIL SURFACE WATER (11). Process functions are not well
characterized but are assumed to be influenced by drying, thermal inactivation and solar
radiation; they are thus most characteristic of pathogens in surface soil.
These preceding 14 compartments are common to most of the five practices modeled.
The following descriptions of the five management practices help clarify the differences
among the practices.
Practice I: Application of Liquid Treated Sludge for Production of Commercial Crops for
Human Consumption.
Liquid sludge may be applied as fertilizer/soil conditioner for the production of
agricultural crops for human consumption or for animal forage or prepared feed. Both
existing (CFR, 1988) and proposed (U.S. EPA, 1989b) regulations prohibit direct application
of sewage sludge to crop surfaces. Therefore, this model practice is designed for a single
application of liquid sludge, which is incorporated into the soil before the crop is planted.
Regulations also require various waiting periods before the planting of crops that will be
consumed uncooked by humans. These restrictions, however, are optional in the model and
can be tested.
Vegetables can be grown aboveground, on-ground or below-ground. These are
represented by tomatoes, zucchini and carrots, respectively. At HARVESTING (15) time,
A-15
-------
all pathogens remaining on CROP SURFACE (14) are transferred to HARVESTING (15),
which represents a single harvest of all of the crop. The same process functions apply as
in CROP SURFACE (14). The crop is held for 24 hours before being processed. The
number of pathogens is then transferred to COMMERCIAL CROPS (16*), the
compartment in which further processing takes place. The number of pathogens/crop unit
following processing is calculated in this compartment and is the figure used in the
vegetable-exposure risk calculations. A 24-hour pathogen exposure is computed by
Subroutine VEG. Pathogen concentrations are determined as number/crop unit for each
sludge management practice. Before being consumed, vegetables normally are processed
in some way. Included in the program is a series of user-selectable processing steps. The
user has the option of choosing any or all processing steps and of specifying some conditions
within processing steps. The human receptor is a person who consumes minimally prepared
vegetables (washed, but not peeled or cooked) at a rate of 81 g tomatoes, 80 g zucchini or
43 g carrots per eating occasion (Pao et al, 1982).
Practice II: Application of Liquid Sludge to Grazed Pastures.
In this practice, liquid sludge is applied as fertilizer, soil conditioner and irrigation
water for the production of forage crops for pasture. This model practice is designed for
repeated applications of liquid sludge, initially on a field with a standing forage crop used
for pasture. It is assumed that spray irrigation will be used because this method is effective
for delivering large amounts of sludge to a large area. In this way, the pasture is also used
as a final treatment and disposal system for the treated sludge. The irrigation rate, the total
weekly depth and the number of times per week can be specified by the user. A sludge
solids concentration of 5% is assumed.
The model assumes that each hectare of pasture supports 12 head of cattle, although
both area and herd size may be varied. This may be a higher density than is the common
practice for fields that receive no irrigation, but with adequate irrigation, sufficient forage
is expected to be produced. Current and proposed regulations require various waiting
periods before animals can be grazed. These requirements can also be tested by the model.
A-16
-------
ANIMAL CONSUMPTION (17) describes the ingestion of CROP SURFACE (14)
by cattle grazing in the pasture. Transfers from ANIMAL CONSUMPTION (17) are to
MEAT (18*), MANURE (19) and MILK (20*).
MEAT (18*) is the compartment describing transfer of pathogens from ANIMAL
CONSUMPTION (17) to meat. The human receptor is assumed to consume 0.256 kg of
meat daily (U.S. FDA, 1978). Contamination of meat by gut contents during slaughter or
by systemic infection by sludge-borne pathogens can be modeled. The model, allows for
inactivation of pathogens in meat by cooking, assuming reasonable cooking times and
temperatures.
The production and consumption of milk from cattle pastured on the sludge-amended
field are modeled when the dairy cattle option is chosen. The default condition is for
consumption of raw milk because commercial production of milk poses an extremely small
hazard of exposure to pathogens. In the model, contamination from dirty,utensils and
careless handling are combined as a transfer from the manure-contaminated udder
[MANURE (19)], which occurs at each milking. All three pathogens can enter milk by this
route. MILK (20*) is the compartment describing production and consumption of milk from
cattle pastured on the sludge-amended field when the dairy cattle option is chosen. The
default condition models the consumption of raw milk that has been stored for 24 hours.
In exposure calculations, it is assumed that the human receptor consumes 2 kg milk/day,
roughly three times the national average milk consumption (U.S. FDA, 1978).
Practice III: Application of Liquid Treated Sludge for Production of Crops Processed
before Animal Consumption.
In this practice, liquid sludge is applied as fertilizer, soil conditioner and irrigation
water for the production of forage crops to be processed and stored for animal feed. This
model practice is designed for repeated applications of liquid sludge, initially on a field with
a standing forage crop. It is assumed that spray irrigation will be used for the application
of liquid sludge, because this method is effective for delivering large amounts of sludge to
a large area. In this way, the field is also used as a final treatment and disposal system for
the treated sludge. The rate, the total weekly depth and the number of irrigations per week
A-17
-------
can be changed by the user. A sludge solids concentration of 5% is assumed. The risks to
the human receptor are similar to those for the preceding practice, i.e., exposure through
meat or milk, in addition to direct contact with the forage grown in the field.
Practice IV: Application of Dried or Composted Sludge to Residential Vegetable Gardens.
Dried or composted treated sludge may be sold or given away to the public as a bulk
or bagged product for use as fertilizer or soil conditioner for the production of domestic
garden crops for human consumption. Although some studies have shown that composting
is highly effective in removing pathogens from sludge (Wiley and Westerberg, 1969), other
studies have shown that bacterial pathogens may grow in dried or composted sludge to
concentrations of IxlO6 organisms/kg dry weight (U.S. EPA, 1988). Exposure of individuals
to materials used in home gardening would be expected to be more frequent than exposure
in a commercial agricultural setting. Therefore, this practice would be expected to pose a
greater risk of infection. This model practice is designed to describe the application of dried
or composted treated sludge, which is incorporated into the soil before the crops are
planted.
Vegetables can be grown aboveground, on-ground or below-ground. These are
represented by tomatoes, zucchini and carrots, respectively. The user may specify the
proportions of above-ground, on-ground and below-ground crops in the garden. At
HARVESTING (15) time, all pathogens remaining on CROP SURFACE (14) are
transferred to HARVESTING (15). The same process functions apply as in CROP
SURFACE (14). The crop is held for 24 hours before being processed. The number of
pathogens is then transferred to CROP (16*), the compartment in which further processing
takes place. The number of pathogens/crop unit following processing is calculated in this
compartment and is the figure used in the vegetable-exposure risk calculations. A 24-hour
pathogen exposure is computed by Subroutine VEG. Pathogen concentrations are
determined as number/crop unit for each sludge management practice. Pathogen
concentrations are determined as number/crop unit.
Before being consumed, vegetables normally are processed in some way. Included
in the program is a series of user-selectable processing steps. The user has the option of
A-18
-------
choosing any or all processing steps and of specifying some conditions within processing
steps. In the default condition, the human receptor is a person who consumes minimally
prepared vegetables (washed, but not peeled or cooked) at a rate of 81 g tomatoes, 80 g
zucchini or 43 g carrots per eating occasion (Pao et al., 1982).
Practice V: Application of Dried or Composted Sludge to Residential Lawns.
Dried or composted treated sludge may be made available to the public as a bulk or
bagged product to be sold or given away for use as fertilizer or soil conditioner for the
preparation of a seed bed for domestic lawns. Individuals engaged in preparing a seed bed
for a lawn are likely to come into contact with the soil and any additives used to improve
the seed bed. If the soil or the additives contain pathogens, this practice would be expected
to pose a risk of infection. This model practice is designed to describe the application of
dried or composted treated sludge, which is incorporated into the soil before the lawn is
seeded.
The main exposure in this practice is for the lawn worker or for a child younger than
5 years old who plays in or walks through the lawn site, incidentally ingesting soil or crop
surface at the daily geometric mean concentration of pathogens. This human receptor
represents the worst-case example of an individual contacting contaminated soil or soiled
clothing or implements. Before all pathogens have been transferred to SOIL SURFACE
(4), exposure is at the pathogen concentration found in undiluted sludge whereas, after the
transfer, the concentration is that calculated for the soil-sludge mixture.
After 840 hours, the time assumed necessary for the lawn to require mowing, the
lawn is mowed weekly, and a fraction of the pathogens associated with CROP SURFACE
(14) are transferred to DIRECT CONTACT (7). It is assumed that the person mowing the
lawn is exposed by inhalation and/or ingestiori at each mowing.
A-19
-------
REFERENCES
Brady, N.C. 1974. The Nature and Properties of Soil. Macmillan Publishing Co., Inc., New
York. p. 266-276.
CFR (Code of Federal Regulations). 1988. Disease, 40 CFR 257.3-6. In: U.S. EPA,
Criteria for classification of solid waste disposal facilities and practices, 40 CFR 257.3.
Crane, S.R. and J.A. Moore. 1986. Modeling enteric bacterial die-off: A review. Water
Air Soil Pollut. 27: 411-439.
Gerba, C.P. 1988. Alternative Procedures for Predicting Viral and Bacterial Transport
from the Subsurface into Groundwater. Draft. U.S. Environmental Protection Agency,
Cincinnati, OH.
Kibbey, H.J., C. Hagedorn and E.L. McCoy. 1978. Use of fecal streptococci as indicators
of pollution in soil. Appl. Environ. Microbiol. 35: 711-717.
Pao, E.M., Fleming, K.H., Guenther, P.M. and Mickle, S.J. 1982. Foods commonly eaten
by individuals: Amount per day and per eating occasion. U.S. Department of Agriculture,
Economics Report No. 44.
USDA (U.S. Department of Agriculture). 1975. Soil taxonomy: A basic system of soil
classification for making and interpreting soil surveys. Soil Conservation Service.
Agriculture Handbook No. 436.
U.S. EPA. 1988. Occurrence of Pathogens in Distribution and Marketing Municipal
Sludges. Prepared by County Sanitation Districts of Los Angeles County for the Health
Effects Research Laboratory, Office of Research and Development, Research Triangle Park,
NC. EPA/600/1-87/014. NTTS PB88 154273.
U.S. EPA. 1989a. Pathogen Risk Assessment for Land Application for Municipal Sludge.
Volume I: Methodology and Computer Model. Volume II: User's Manual. Office of
Health and Environmental Assessment, Environmental Criteria and Assessment Office,
Cincinnati, OH. EPA/600/6-90/002A,B. NTIS PB90-171901 and PB90-171919.
U.S. EPA. 1989b. Standards for the Disposal of Sewage Sludge; Proposed Rule. Federal
Register 54(23): 5886-5887.
U.S.'FDA (U.S. Food and Drug Administration). 1978. FY78 Total Diet Studies.
van Genuchten, M.T. and WJ. Alves. 1982. Analytical solutions on the one-dimensional
convective-dispersive solute transport equation. USDA Technical Bulletin No. 1661.
Wiley, B.B. and S.C. Westerberg. 1969. Survival of human pathogens in composted sewage.
Appl. Microbiol. 18: 994-1001.
A-20
-------
APPENDIX B
MAXIMUM PROBABILITY OF INFECTION
SITE 1, PRACTICE I
B-l
-------
-------
File
Maximum Probability of Infection
Site 1, Practice I
ONSITE OFFSITE EATER DRINKER SWIMMER
OOOOA1
0101A1
0102A1
0202A1
0203A1
0602A1
0603A1
0702A1
0703A1
1001A1
1002A1
1004A1
1005A1
1006A1
1007A1
1008A1
1009A1
1202A1
1203A1
1701A1
1702A1
1703A1
1704A1
1705A1
1706A1
1707A1
1708A1
1709A1
1710A1
1711A1
1712A1
1802A1
1803A1
1804A1
1902A1
1903A1
1.11E-16
1.11E-16
6.83E-03
1.12E-08
l.OOE+00
O.OOE+00
6.83E-03
9.99E-16
3.00E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
9.80E-01
2.22E-16
6.83E-03
6.84E-03
6.83E-03
6.84E-03
6.83E^03
6.84E-03
6.83E-03
6.84E-03
6.83E-03
6.84E-03
6.83E-03
6.84E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.84E-03
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
OiOOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.64E-06
1.11E-16
1.11E-16
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
O.OOE + 00
1.11E-16
1.11E-16
1.40E-08
2.55E-15
2.81E-01
O.OOE+00
1.40E-08
O.OOE+00
1.11E-16
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
5.50E-01
2.22E-16
1.40E-08
1.28E-08
1.40E-08
1.28E-08
1.40E-08
1.28E-08
1.40E-08
1.28E-08
1.40E-08
1.28E-08
1.40E-08
1.28E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.28E-08
B-3
-------
File
APPENDIX B
Maximum Probability of Infection
Site 1, Practice I (Continued)
ONSITE OFFSITE EATER DRINKER SWIMMER
2001A1
2002A1
2003A1
2004A1
2005A1
2006A1
2007A1
2008A1
2009A1
2101A1
2102A1
2103A1
2104A1
2105A1
2106A1
2302A1
2303A1
2402A1
2403A1
2502A1
2503A1
2601A1
2602A1
2603A1
2604A1
2605A1
2606A1
2607A1
2608A1
2609A1
2802A1
2803A1
2901A1
2902A1
2903A1
2905A1
2906A1
2907A1
6.83E-03
6.83E-03
6.83E-03
6.84E-03
6.84E-03
6.84E-03
6.84E-03
6.84E-03
6.84E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.84E-03
6.84E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
5.14E-03
8.72E-03
4.80E-03
4.38E-03
5.21E-03
8.72E-03
5.77E-03
1.23E-02
1.08E-02
5.05E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
-O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
O.OOE + 00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16 '
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.40E-08
1.40E-08
1.40E-08
1.41E-08
1.41E-08
1.41E-08
1.41E-08
1.41E-08
1.41E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
B-4
-------
File
APPENDIX B
Maximum Probability of Infection
Site 1, Practice I (Continued)
ONSITE OFFSITE EATER DRINKER SWIMMER
2908A1
2909A1
3001A1
3002A1
3101A1
3102A1
3103A1
3104A1
3105A1
3106A1
3202A1
3203A1
3204A1
3301A1
3302A1
3303A1
3304A1
3305A1
3306A1
3501A1
3502A1
3503A1
3504A1
3505A1
3506A1
3507A1
3508A1
3509A1
3510A1
3701A1
3702A1
3703A1
3704A1
3705A1
3706A1
3707A1
3708A1
3709A1
6.83E-03
6.83E-03
6.83E-03
6.83E-03
O.OOE + 00
6.83E-03
O.OOE+00
6.83E-03
O.OOE + 00
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
4.17E-02
4.78E-04
5.47E-01
6.85E-01
3.70E-01
6.14E-05
5.42E-04
1.01E-06
7.98E-02
6.83E-03
8.5 IE- 13
9.54E-08
6.83E-03
8.51E-13
1.76E-07
6.83E-03
8.51E-13
1.76E-07
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE + 00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
O.OOE+00
O.OOE+00
1.11E-16
O.OOE+00
O.OOE+00
1.11E-16
O.OOE+00
O.OOE+00
1.40E-08
1.40E-08
1.40E-08
6.68E-09
O.OOE+00
1.40E-08
O.OOE+00
1.40E-08
O.OOE+00
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.32E-05
9.58E-13
2.90E-01
6.15E-01
6.00E-02
1.44E-15
2.66E-12
2.22E-16
1.70E-04
1.40E-08
O.OOE+00
1.11E-16
1.40E-08
O.OOE+00
1.11E-16
1.40E-08
O.OOE+00
1.11E-16
B-5
-------
File
APPENDIX B
Maximum Probability of Infection
Site 1, Practice I (Continued)
ONSITE OFFSITE EATER DRINKER SWIMMER
4102A1
4103A1
4202A1
4402A1
4403A1
4502A1
4503A1
4601A1
4602A1
4603A1
4701A1
4702A1
4703A1
4801A1
4802A1
4803A1
4901A1
4902A1
4903A1
5001A1
5002A1
5003A1
5101A1
5102A1
5201A1
5202A1
5302A1
5303A1
6602A1
6603A1
6701A1
7001A1
7002A1
7003A1
7101A1
7102A1
7103A1
6.83E-03
6.83E-03
6.83E-03
6.84E-03
5.45E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
L11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.40E-08
1.40E-08
1.40E-08
1.41E-08
5.01E-09
1.45E-08
6.74E-09
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
B-6
-------
File
APPENDIX B
Maximum Probability of Infection
Site 1, Practice I (Continued)
ONSITE OFFSITE EATER DRINKER SWIMMER
7201A1
7202A1
7203A1
G0202A1
G0203A1
G0302A1
G0303A1
G0402A1
G0403A1
G1001A1
G1003A1
P0001A1
P0002A1
P0003A1
P0004A1
P0005A1
P0006A1
P0007A1
P0008A1
P0009A1
P0010A1
P0011A1
P0012A1
P0013A1
P0014A1
P0015A1 .
R0502A1
R0503A1
R0602A1
R0603A1
R0802A1
R1102A1
R3202A1
R3203A1
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
9.76E-01
9.76E-01
9.76E-01
7.48E-01
7.48E-01
7.48E-01
8.56E-03
8.56E-03
8.56E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
l.OOE+00
6.83E-03
6.83E-03
6.83E-03
1.21E-07
l.OOE+00
6.83E-03
6.83E-03
6.83E-03
6.83E-03
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O'.OOE+OO
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
O.OOE + 00
1.11E-16
1.11E-16
2.22E-16
2.22E-16
2.22E-16
1.11E-16
1.11E-16
1.11E-16
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
l.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
l.OOE+00
l.OOE + 00
l.OOE + 00
9.80E-01
9.80E-01
9.80E-01
8.64E-09
8.64E-09
8.64E-09
1.40E-08
1.40E-08
1.40E-08
1.40E-08
l.OOE+00
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
B-7
-------
File
APPENDIX B
Maximum Probability of Infection
Site 1, Practice I (Continued)
ONSITE OFFSITE EATER
DRINKER SWIMMER
R3302A1
R3303A1
R3501A1
R3502A1
R3503A1
R3504A1
R3505A1
R3506A1
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
6.83E-03
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE + 00
O.OOE+00
O.OOE+00
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
1.11E-16
8.00E-03
2.22E-16
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
1.40E-08
B-8
-------
APPENDIX C
SAMPLE MAXIMUM AND CUMULATIVE PROBABILITY
OF INFECTION AND MAXIMUM CONTENTS OF COMPARTMENTS
C-l
-------
-------
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = OOOOA1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
3
0
0
0
9
1.11E-16
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
CUMULATIVE
2.22E-16
O.OOE+00
O.OOE+00
O.OOE+00
5.55E-16
MAXIMUM GROUNDWATER FLUX = 2.49E-01 ON DAY
61
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
HOUR
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
5.00E+08
1.69E+08
2.88E+02
1.61E+08
O.OOE+00
1.11E+04
69E-03
02E+04
02E+01
5.02E+01
1.05E+05
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
2,
5,
5,
1
25
25
27
0
81
28
38
39
40
87
0
0
0
0
0
C-3
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = 0101A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
3
0
0
0
8
1.11E-16
O.OOE+00
O.OOE+00
O.OOE+00
1.11B-16
CUMULATIVE
6.66E-16
O.OOE+00
O.OOE+00
O.OOE+00
2.22E-16
MAXIMUM GROUNDWATER FLUX
O.OOE+00 ON DAY
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1,
3,
.OOE+06
.38E+05
5.75E-01
3.23E+05
O.OOE+00
2.22E+01
5.38E-06
l.OOE+02
O.OOE+00
O.OOE+00
2.10E+02
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
HOUR
1
25
25
27
0
81
28
38
0
0
87
0
0
0
0
0
C-4
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME - 0102A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
2
0
0
53
5
6.83E-03
O.OOE+00
O.QOE+00
1.11E-16
1.40E-08
CUMULATIVE
6.83E-03
O.OOE+00
O.OOE+00
2.22E-16
2.14E-08
MAXIMUM GROUNDWATER FLUX = 2.49E+01 ON DAY 61
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
HOUR
5.00E+10
1.69E+10
2.88E+04
1.61E+10
O.OOE+00
1.11E+06
2.69E-01
5.02E+06
5.02E+03
5.02E+03
1.05E+07
2.46E+01
O.OOE+00
1.97E+00
O.OOE+00
O.OOE+00
1
25
25
27
0
81
28
38
39
40
87
1441
0
260
0
0
C-5
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = 0202A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
2
0
0
0
5
1.12E-08
O.OOE+00
Q.OOE+00
O.OOE+00
2.55E-15
CUMULATIVE
1.12E-08
O.OOE+00
O.OOE+00
O.OOE+00
4.22E-15
MAXIMUM GROUNDWATER FLUX = 4.97E+00 ON DAY 61
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
l.OOE+10
3.38E+09
5.75E+03
3.23E+09
O.OOE+00
2.22E+05
5.38E-02
l.OOE+06
l.OOE+03
l.OOE+03
2.10E+06
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
HOUR
1
25
25
27
0
81
28
38
39
40
87
0
0
0
0
0
C-6
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = 0203A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
2
0
0
49
5
l.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
2.81E-01
CUMULATIVE
l.OOE+00
O.OOE+00
O.OOE+00
7.77E-16
5.88E-01
MAXIMUM GROUNDWATER FLUX = 2.49E+02 ON DAY 61
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
HOUR
5.00E+11
1.69E+11
2.88E+05
1.61E+11
O.OOE+00
1.11E+07
2.69E+00
5.02E+07
5.02E+04
5.02E+04
1.05E+08
2.46E+02
O.OOE+00
1.97E+01
3.48E+00
O.OOE+00
1
25
25
27
0
81
28
38
39
40
87
1441
0
260
301
0
C-7
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = 0602A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
0
0
0
44
0
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
O.OOE+00
CUMULATIVE
O.OOE+00
O.OOE+00
O.OOE+00
1.55E-15
O.OOE+00
MAXIMUM GROUNDWATER FLUX = 5.84E+04 ON DAY
61
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
HOUR
5.00E+10
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
3.27E+10
1.19E+07
1.19E+07
O.OOE+00
5.77E+04
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
1
0
0
0
0
0
.0
11
39
40
0
1441
0
0
0
0
C-8
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME,= 0603A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
2
0
0
53
5
6.83E-03
O.OOE+00
O.OOE+00
1.11E-16
1.40E-08
CUMULATIVE
6.83E-03
O.OOE+00
O.OOE+00
2.22E-16
2.14E-08
MAXIMUM GROUNDWATER FLUX = 2.49E+01 ON DAY
61
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
HOUR
5.00E+10
1.69E+10
2.88E+04
1.61E+10
O.OOE+00
1.11E+06
2.6.9E-01
5.02E+06
5.02E+03
5.02E+03
1.05E+07
2.46E+01
O.OOE+00
1.97E+00
O.OOE+00
O.OOE+00
1
25
25
27
0
81
28
38
39
40
87
1441
0
260
0
0
C-9
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = 0702A1
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
2
0
0
0
0
9.99E-16
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
CUMULATIVE
9.99E-16
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
MAXIMUM GROUNDWATER FLUX = O.OOE+00 ON DAY
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
5.00E+10
1.69E+10
2.88E+04
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
O.OOE+00
HOUR
1
25
25
0
0
0
0
0
0
0
0
0
0
0
0
0
C-10
-------
BACTERIA, SITE 1, PRACTICE 1
SUMMARY OF REPORTS FOR PATHOGEN RISK ASSESSMENT
MODEL RUN, FILENAME = 0703Al
PROBABILITY OF INFECTION
PATH DAY MAXIMUM
1
2
3
4
5
2
0
0
0
14
3.00E-03
O.OOE+00
O.OOE+00
O.OOE+00
1.11E-16
CUMULATIVE
9.01E-03
O.OOE+00
O.OOE+00
O.OOE+00
6.66E-16
MAXIMUM GROUNDWATER FLUX = 4.33E-01 ON DAY 65
MAXIMUM PATHOGEN NUMBER:
COMPARTMENT NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
5.00E+10
1.69E+10
2.88E+04
3.81E+08
O.OOE+00
8.13E+03
6.35E-03
8.73E+04
8.73E+01
8.73E+01
7.75E+04
O.OOE+00
O.OOE+00
2.01E+00
O.OOE+00
O.OOE+00
HOUR
1
25
25
105
0
178
106
122
123
124
171
0
0
260
0
0
*U.S. GOVERNMENT HUNTING OFFICE: 1991 -5 mi- 1S7<» 0601
C-ll
-------
-------
-------
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