land application of manure and class b biosolids: an...

2009

Land application is a practical use of municipal Class B biosolids and manure that also promotes soil fertility and productivity. To date, no study exists comparing biosolids to manure microbial risks. Th is study used quantitative microbial risk assessment to estimate pathogen risks from occupational and public exposures during scenarios involving fomite, soil, crop, and aerosol exposures. Greatest one-time risks were from direct consumption of contaminated soil or exposure to fomites, with one-time risks greater than 10−1. Recent contamination and high exposures doses increased most risks. Campylobacter jejuni and enteric viruses provided the greatest single risks for most scenarios, particularly in the short term. All pathogen risks were decreased with time, 1 d to14 mo between land application and exposure; decreases in risk were typically over six orders of magnitude beyond 30 d. Nearly all risks were reduced to below 10−4 when using a 4-mo harvest delay for crop consumption. Occupational, more direct risks were greater than indirect public risks, which oft en occur aft er time and dilution have reduced pathogen loads to tolerable levels. Comparison of risks by pathogen group confi rmed greater bacterial risks from manure, whereas viral risks were exclusive to biosolids. A direct comparison of the two residual types showed that biosolids use had greater risk because of the high infectivity of viruses, whereas the presence of environmentally recalcitrant pathogens such as Cryptosporidium and Listeria maintained manure risk. Direct comparisons of shared pathogens resulted in greater manure risks. Overall, it appears that in the short term, risks were high for both types of residuals, but given treatment, attenuation, and dilution, risks can be reduced to near-insignifi cant levels. Th at being said, limited data sets, dose exposures, site-specifi c inactivation rates, pathogen spikes, environmental change, regrowth, and wildlife will increase risk and uncertainty and remain areas poorly understood.

Land Application of Manure and Class B Biosolids: An Occupational and Public Quantitative Microbial Risk Assessment

John P. Brooks,* Michael R. McLaughlin, Charles P. Gerba, and Ian L. Pepper

The majority of animal manures and municipal bio-solids produced in the United States are utilized as soil nutrient amendments for forage and crop production

(National Research Council, 2002; Burkholder et al., 2007). In the United States, approximately 450,000 animal feeding operations (AFOs) or concentrated animal feeding operations (CAFOs), produce approximately 100 million dry Mg of manure per year (Burkholder et al., 2007). Manures are generally not treated beyond storage, which results in quasi-treatment before land application (Kelley et al., 1994; Pell, 1997; Hutchison et al., 2004, 2005a,b; Mannion et al., 2007; Brooks et al., 2009a), in contrast to municipal biosolids, which are always treated to some extent (USEPA, 1993). Approximately 16,000 municipal wastewater treatment plants operate in the U.S. producing a total of 6.7 million dry Mg of bio-solids annually (National Research Council, 2002). A requirement for the utilization of these residual wastes is proper environmental stewardship; however, the presence of enteric microbial pathogens can compromise the land application process, for the occupational- and public-exposed. Mead et al. (1999) estimated that 76 million cases of foodborne illness occur annually in the United States, of which 38 million could be attributed to known etiological agents. Scallan et al. (2011) recently estimated 9.4 million cases from known etiological agents and 38.4 million from unknown agents. Among the known pathogens, Campylobacter jejuni, Salmonella spp., Clostridium, and norovirus account for 60 to 70% of all out-breaks (Mead et al., 1999; Scallan et al., 2011).

Animal feces or runoff carrying manure-borne microbial patho-gens have been implicated in some of the largest water- and food-borne outbreaks in recent years (Curriero et al., 2001; Hrudey et al., 2003). Th e Salinas Valley, CA, Milwaukee, WI, and Walkerton, ON (Canada), outbreaks documented animal manure-borne food-crop and drinking water outbreaks (Hoxie et al., 1997; Hrudey et

Abbreviations: AFO, animal feeding operation; BGM, Buff alo green monkey; CAFO;

concentrated animal feeding operation; CFU, colony-forming unit; GU, genomic

units; PFU, plaque-forming unit; QMRA, quantitative microbial risk assessment;

qPCR, quantitative polymerase chain reaction.

J.P. Brooks and M.R. McLaughlin, USDA–ARS, Genetics and Precision Agriculture

Unit, P.O. Box 5367, Mississippi State, MS 39762. C.P. Gerba and I.L. Pepper, Dep.

of Soil Water and Environmental Science, Univ. of Arizona, Tucson, AZ 85721.

Approved for publication as Journal Article No. J–12091 of the Mississippi

Agricultural and Forestry Experiment Station, Mississippi State University. Mention

of a trade name, proprietary product, or specifi c equipment does not constitute

a guarantee or warranty by the USDA and does not imply its approval to the

exclusion of other products that may be suitable. This work was prepared by

employees of the U.S. Government as part of their offi cial duties and is in the

public domain and may be used without further permission. Assigned to Associate

Editor Ed Topp.

Copyright © American Society of Agronomy, Crop Science Society of America, and

Soil Science Society of America. All rights reserved. No part of this periodical may

be reproduced or transmitted in any form or by any means, electronic or mechani-

cal, including photocopying, recording, or any information storage and retrieval

system, without permission in writing from the publisher.

J. Environ. Qual. 41

doi:10.2134/jeq2011.0430

Received 14 Nov. 2011.

*Corresponding author ([email protected]).

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al., 2003; FDA, 2006). In all three cases, land-applied manure was not necessarily the implicated source; however, runoff and feral animals were determined to be infectious vectors. Both residuals (manure and Class B biosolids) potentially contain a wide range of pathogens, some common to both and others specifi c to each residual. Depending on its origin, manure can be a source of C. jejuni, Escherichia coli O157:H7, Salmonella spp., Listeria mono-cytogenes, Cryptosporidium parvum, and Giardia lamblia (Guan and Holley, 2003; Hutchison et al., 2004, 2005a; McLaughlin et al., 2009). Municipal Class B biosolids can additionally contain human viruses, depending on municipality and treatment (Straub et al., 1993; Viau and Peccia, 2009; Pepper et al., 2010; Wong et al., 2010). Viruses of the enterovirus group (enterovirus 68–101, Coxsackievirus, poliovirus, and echovirus), rotavirus, adenovirus, and norovirus can all be present in Class B biosolids.

Th ere is concern about the presence of zoonotic and anthro-ponotic pathogens in the environment and in the food and water supplies. Few regulations are in place to monitor manure land application, especially with respect to pathogens. Recommenda-tions or best management practices are available but are not actual regulations (USDA–AMS, 2000). In the United States, manure can be used for food crops, regardless of pathogen load, provided that a recommended minimum 90- to 120-d harvest delay is fol-lowed (USDA–AMS, 2000). In contrast, biosolids are regulated, on pathogen load and harvest delays, by the USEPA Part 503 rule (USEPA, 1993) and Th e Safe Sludge Matrix in the United Kingdom (ADAS, 2001). Regulations and recommendations are meant to be protective of public health by limiting human contact with land-applied residual pathogens.

Recent studies have attempted to quantify occupational and public microbial risks associated with land-applied biosolids (Dowd et al., 2000; Eisenberg et al., 2004, 2008; Gale, 2005; Brooks et al., 2005, 2009b; Tanner et al., 2008; Viau et al., 2011). Risks have ranged from low (<10−12 per year) to as high as 1 (100%), for “worst-case” conditions (e.g., minimal personal protective equip-ment, high pathogen load, high wind speed). Studies by Dowd et al. (2000), Brooks et al. (2005), Tanner et al. (2008), and Viau et al. (2011) focused on aerosol transport of various pathogens (e.g., Salmonella, norovirus) downwind to persons near the application site. In all studies, the greatest risk was to those working on or near the application sites, whereas “downstream” eff ects such as future consumption of products grown on application sites yielded low risks (Gale, 2005). To date, no study has attempted to quantify risks associated with manure land application, nor have there been spe-cifi c comparisons of the relative risks of these two residuals.

Th is study uses a quantitative microbial risk assessment (QMRA) approach to evaluate and quantify the relative occu-pational (direct exposure) and public (indirect, incidental exposure) risks of pathogens in biosolids and animal manure applied to land. Th e four basic steps include (National Research Council, 1983): pathogen identifi cation, exposure assessment, dose–response, and risk characterization. Microbial quality data from animal manure and Class B biosolids were gathered from published sources. Specifi c, conservative exposure scenarios were simulated to determine microbial risks. Risks are presented as a function of low to high pathogen levels in each residual/expo-sure scenario and are expressed as risk of infection (e.g., 10−4 is a 0.01% chance of infection). Each scenario is presented as an independent simulation and should be considered separately

from one another. Figures 1 and 2 illustrate an example simula-tion and the pathways simulated herein, respectively.

Materials and Methods

Class B Biosolids, Manure, and Raw Sludge

Pathogen LevelsLow to high pathogen levels in manures and mesophillic

anaerobic digested Class B biosolids are shown in Table 1; patho-gen presence was assumed, although “zero” and below detection levels are oft en the case. Pathogens were chosen on the basis of presence in the literature, dose–response availability, and dis-ease incidence in the population. Campylobacter jejuni, E. coli O157:H7, L. monocytogenes, Salmonella spp., adenovirus, enteric viruses (Coxsackievirus and rotavirus), norovirus, and C. parvum were estimated in bovine , poultry, and swine manure, raw sludge, and Class B biosolids. For the current study, depending on the risk scenario, enteroviruses were considered as either Coxsacki-evirus (inhalation) or rotavirus (ingestion) (Gerba et al., 2002; Gale 2005). Pathogen data were selected in an attempt to collect as many sources from the previous 30 yr involving naturally con-taminated residual sources. An attempt was made to collect cul-

Fig. 1. Schematic based on and modifi ed from Gale (2005), demon-stration of the quantitative microbial risk assessment process for Escherichia coli O157:H7 soil ingestion, which illustrates the eff ect on the pathogen level and hence risk associated with each parameter or step in the land application and exposure process. CFU, colony-forming unit.

Fig. 2. Schematic depicting the potential routes of exposure from a residual land application event. Dashed, elbow connectors repre-sent secondary contamination, in which the primary contaminated matrix proceeds to contaminate the next matrix. Boxes with dashed lines represent poorly understood areas of the quantitative micro-bial risk assessment. † represents routes of exposure modeled in the current study.

www.agronomy.org • www.crops.org • www.soils.org 2011

ture data as the majority of the dose–response curves are based on live infectious units; however, in some cases, molecular data was used. Viau and Peccia (2009) and Wong et al. (2010) presented quantitative polymerase chain reaction (qPCR) data, reported in genomic units (GU). Molecular methods such as qPCR do not distinguish between inactive and infectious bacterial or viral units; thus, a value for adenovirus of 4.3 × 105 GU g−1 biosolids would theoretically represent both inactive and infectious viral particles.

To use this data in dose–response models, a ratio of 1000:1 GU to infectious units was used (Viau et al., 2011); thus, it was esti-mated that 4.3 × 105 GU g−1 would represent 4.3 × 102 infectious adenovirus particles. In the case of norovirus, the dose–response model was resolved with GU; therefore, only the biosolids pro-cess was assumed to increase the viral GU to infectious unit ratio. Monpoeho et al. (2004) and Wong et al. (2010) measured ~50:1 to 1000:1 enterovirus GU:infectious units following biosolids

Table 1. Dose–response parameters and pathogen levels for each pathogen/residual combination used in the quantitative microbial risk assessment. Pathogen levels are presented as a range of low to high residual levels as reported in the cited references.

Organism, dose–response parameters†

Manure or biosolids pathogen level

Residual sourceRange CFU or PFU dry g−1‡

ReferencesLow–High

Bacteria

Campylobacter jejuniα = 1.5 × 10−1, β = 7.6 × 10°

(Medema et al., 1996)

Bovine 4.1 × 101–5.3 × 102 Hutchison et al., 2005a

Poultry 1.5 × 101–7.6 × 103 Hutchison et al., 2005a; Kelley et al., 1994

Swine 4.2 × 101–5.1 × 103 Chinivasagam et al., 2004; Hutchison et al., 2005a; McLaughlin et al., 2009

Raw sludge 2.6 × 103–3.8 × 103 Betaieb and Jones, 1990; Jones et al., 1990

Class B biosolids 1.6 × 10°Betaieb and Jones, 1990; Jones et al., 1990; Pepper et al., 2010

E. coli O157:H7α = 1.62 × 10−1, β = 1.57 × 101

(Crockett et al., 1996; Strachan et al., 2005)Bovine 2.4 × 101–1.2 × 103 Berry and Miller, 2005; Hutchison et al., 2005a

Listeria monocytogenesα = 2.5 × 10−1, N

50 = 2.8 × 102

(Haas and Thayyar-Madabusi, 1999)


Poultry 4.0 × 101–8.3 × 102 Hutchison et al., 2005a; Brooks and McLaughlin, 2008

Swine 1.4 × 101–3.1 × 103 Hutchison et al., 2005a; McLaughlin et al., 2009

Raw sludge 2.3 × 102–1.5 × 103 Garrec et al., 2003

Class B biosolids 8.3 × 10−1–2.6 × 101 Garrec et al., 2003

Salmonella spp.α = 3.1 × 10−1, N

50 = 2.4 × 104

(Haas et al., 1999)


Poultry 9.6 × 10−1–4.0 × 103 Brooks and McLaughlin, 2008; Hutchison et al., 2005a

Swine 6.2 × 10°–4.1 × 102

Chinivasagam et al., 2004; Hutchison et al., 2005a; McLaughlin et al., 2009; Vanotti et al., 2005

Raw sludge 5.1 × 102–1.1 × 104 Pepper et al., 2010; Gerba et al., 2008

Class B biosolids 5.0 × 10°–1.1 × 102 Pepper et al., 2010; Gerba et al., 2008

Virus

Adenovirusr = 4.2 × 10−1 (Crabtree et al., 1997)

Raw sludge 2.8 × 101–5.8 × 102 Viau and Peccia, 2009§, Pepper et al., 2010

Class B biosolids 3.7 × 10°–4.3 × 102 Viau and Peccia, 2009§; Pepper et al., 2010

Enteroviruses¶r = 1.5 × 10−2 (α = 2.65 × 10−1, N

50 = 5.6)

(Haas et al., 1999)

Raw sludge 4.8 × 10°–3.5 × 102 Soares et al., 1994; Guzman et al., 2007; Pepper et al., 2010

Class B biosolids 1.0 × 10−1–1.3 × 102 Soares et al., 1994; Guzman et al., 2007; Pepper et al., 2010

Norovirusα = 0.040, β = 0.055, a = 0.9997

(Teunis et al., 2008)

Raw sludge 3.6 × 103–2.0 × 107 Wong et al., 2010§

Class B biosolids 1.0 × 103–3.0 × 103 Wong et al., 2010§

Parasite

Cryptosporidium parvumr = 5.7 × 10−2

(Messner et al., 2001)

Bovine 2.8 × 10−1–1.8 × 102 Heitman et al., 2002; Hutchison et al., 2005a; Atwill et al., 2006; Lalancette et al., 2012

Raw sludge 1.3 × 101–6.4 × 101 Guzman et al., 2007

Class B biosolids 7.4 × 10−2–6.7 × 10° Guzman et al., 2007

† Dose–response factors: α, r, β, a, and N50

describe infectivity of the pathogen. Norovirus dose–response curve is based on genomic units. All others are

based on culturable/infectious units.

‡ CFU, colony-forming unit; PFU, plaque-forming unit. Ranges were presented as lower and upper bound positive values assuming pathogen presence

in the residual; it is possible that a residual source could be below detection limits or 0 g−1, which would yield insignifi cant risks.

§ Viau and Peccia (2009) and Wong et al. (2010) data were converted from genomic unit g−1 to infectious unit g−1 using a 1000:1 and 50:1

genomic:infectious unit ratio, respectively.

¶ For this analysis, aerosol exposures comprised exposure to Coxsackievirus (r = 1.5 × 10−2), and ingestion exposures comprised exposures to rotavirus

(α = 2.65 × 10−1, N50

= 5.6).


digestion and was used as the basis for the norovirus ratio (50:1). In most situations, exclusive use of genomic data would overesti-mate the risk. Likewise, risks may be underestimated while exclu-sively using culture data, as it is highly unlikely that all infectious or live particles or units will be included (Handelsman, 2004).

Data collected by Hutchison et al. (2005a) were originally presented as mean pathogen levels of positive samples only. Using these means overestimated pathogen levels because negatives were not considered. For example, the Hutchison et al. (2005a) value of 1.1 × 103 colony-forming units (CFU) g−1 reported for Liste-ria spp. in cattle manures represented the mean of only positive samples (29.8%). For the current study, the reported values were “weighted” by multiplying by the percent positives (Soller et al., 2010). Th is manipulation accounts for samples below detection limits and reduces the Listeria spp. value to 3.28 × 102 CFU g−1. Raw sludge adenovirus levels were estimated by back-calculating from Class B biosolids levels using log10 reduction effi ciencies pre-viously reported for mesophillic anaerobic digestion processes; 0.82 for enteric viruses (Epstein, 2003).

Pathogen InactivationGale (2005) developed an eloquent paradigm to estimate con-

tamination of fresh food crops with Class B biosolids-borne patho-gens. Th e current study expanded the approach to include various occupational and public exposures, such as fomites, soil, crop, runoff , and bioaerosols.

Fomite ContaminationFomite contamination events were estimated by fi rst assuming

0.1 g of waste residual transferred to a fomite (e.g., truck or tractor handle):

fc = rc × ft [1]

where fc is fomite pathogen concentrations in CFU, plaque-form-ing unit (PFU), or oocyst fomite−1; rc is residual pathogen con-centration g−1; and ft is amount of residual transferred to a fomite (residual g). For single-day exposures, no decay was assumed to occur, and contact was assumed to be immediate or within the work day. For fomite exposures occurring following pathogen decay, Eq. [1] was further modifi ed to estimate the eff ects of decay over time:

fc = rc × ft × (1/10df ) [2]

where df is pathogen decay on a fomite over time (log reduction d−1). Abad et al. (1994), Panagea et al. (2005), and Masago et al. (2008) reported log reduction rates for poliovirus (5.2 log10 4 d−1) and adenovirus (4.8 log10 4 d−1), Pseudomonas aerugi-nosa (Gram-negative) (4.5 log10 d−1), and Staphylococcus aureus (Gram-positive) (0.009 log10 0.042 d−1). Each log reduction was assumed to follow fi rst order decay kinetics and was linearly extrapolated from 0 to 30 d.

Soil ContaminationEquation [3] was used to calculate pathogen levels in soil follow-

ing residual land application events and subsequent levels following pathogen decay over time:

sc = rc × dr × (1/10sr) ×1000 [3]

where sc is the soil pathogen concentration expressed as CFU, PFU, or oocyst kg−1 soil, dr is the soil dilution ratio; and sr is the soil decay rate (as log reductions for a given time). For this simulation, soil dilution was set as 1.75 × 10−3 (0.00175 g of residual per g of residual/soil mixture), equivalent to an appli-cation rate of 6.75 Mg (dry) ha−1 (~2.47 acres) (Gale, 2005) that is subsequently disked to a depth of ≥15 cm within 24 h, as commonly practiced (Gale, 2005). Pathogen decay rates in soil (sr) were obtained from the literature: Campylobacter (2.4 log10 16 d−1) (Hutchison et al., 2005b), E. coli O157:H7 (4.5 log10 50 d−1) (Bolton et al., 1999), L. monocytogenes (3.0 log10 30 d−1) (Hutchison et al., 2005b), Salmonella spp. (2.1 log10 35 d−1) (Watkins and Sleath, 1981), adenovirus (3.0 log10 20 d−1) (Davies et al., 2006), poliovirus and norovirus (4.0 log10 90 d−1) (Tier-ney et al., 1977), and C. parvum (3.0 log10 63 d−1) (Hutchison et al., 2005b). Decay rates were assumed to follow fi rst-order decay kinetics and were linearly extrapolated from 1 d to 38 mo.

Crop ContaminationA further modifi cation of Eq. [3] allows estimation of pathogen

concentrations on food crops (cc), grown touching the soil surface (i.e., lettuce) (Eq. [4]):

cc = rc × dr × (1/10sr) × rr × wr × 1000 [4]

where cc is crop pathogen concentration (pathogen kg−1); rr is the percentage (2%) of soil particles remaining on a crop following harvest; and wr is the percentage (10%) of soil particles remaining on a crop following washing, according to (Gale, 2005).

Crop Contamination from Runoff Pathogens in runoff were estimated by fi rst quantifying the total

number of pathogens deposited onto a fi eld following a land appli-cation event, using Eq. [5]:

pc = rc × ar [5]

where pc is the plot pathogen concentration (pathogen ha−1); rc is the residual concentration per g (solid application) or mL (liquid application) multiplied by 1 × 106 or 1 × 103 to convert to Mg or L, respectively; and ar is the application rate (6.57 Mg ha−1 for solid application and 2.54 ha-cm, e.g., 2.54 × 105 L ha−1 for liquid appli-cation) (Adeli et al., 2005; Gale, 2005). Equation [6] estimated the pathogen concentration on runoff contaminated crops:

rfc = pc × lr × tr × dw × (1/10iw) × tc × (1/10ic) × wr [6]

where rfc is the pathogen concentration on crops contaminated with runoff water (pathogen kg−1); lr is amount of residual available for runoff ; tr is the percent of pathogen transfer from applied residual to runoff water; dw is the pathogen dilution rate into irrigation water (runoff volume (L ha−1 per irrigation water volume L ha−1); iw is the pathogen inactivation rate in water; tc is percentage of pathogen transfer from irrigation water to crop; and ic is the pathogen inactivation rate on crops. Th e amount of residual available for runoff (lr) was conservatively estimated to be 10%. In this simulated scenario, transfer rates of land-applied pathogens to runoff water (tr) were assumed to be 1.6% in a given area (3% slope) assuming a rainfall of 75 mm h−1 over 0.5 h (Brooks et al., 2012). It was assumed that the pathogen runoff


load would be diluted once contact with an adjacent irrigation canal was made and that this dilution (dw) would reduce patho-gen levels as represented in Eq. [7]:

dw = runoff volume/irrigation water volume [7]

where water runoff volume and irrigation water volume are in L ha−1. Runoff volumes based on the above assumptions were esti-mated at approximately 2.0 × 104 L ha−1 (Brooks et al., 2012), and irrigation water volumes for lettuce (442,000 L ha−1) were estimated following the Stine et al. (2005a) paradigm. Pathogen inactivations (iw) while in the irrigation canal were 0.24 log10 d

−1 (Anderson et al. 2005). According to Stine et al. (2005a), pathogen impact and attachment to leafy crops (tc) was assumed at 1.1 × 10−4% (bacte-ria) and 0.046% (viruses). Inactivation while on the same crop (ic) was modeled according to Stine et al. (2005b), 4.9 (E. coli and C. jejuni), 0.06 (S. enterica), 2.46 (L. monocytogenes), 0.25 (adenovi-rus), 0.29 (enteroviruses), 1.06 (norovirus), and 0.10 (C. parvum).

Aerosol Contamination during Initial ApplicationPathogen aerosolization effi ciency from residual to air was

estimated using an aerosolized pathogen ratio of 1:1000 (aero-solized pathogens/residual pathogens) at the source (Paez-Rubio et al., 2007). Th is was used to describe the initial pathogen con-centration at the source (pathogen m−3). Downwind transport of aerosolized pathogens was then simulated using an empiri-cally derived linear equation previously defi ned by Brooks et al. (2005) as shown in Eq. [8]:

ac = {[(−0.0022×dis) + 0.1849]×16.6 [8]

where ac is aerosolized pathogen concentration (pathogen m−3), and dis is downwind distance from 2 to 100 m downwind of the land application operation assuming constant wind conditions of 2.2 m s−1.

Dose ExposurePathogen exposures for each simulation were estimated for each

exposure scenario with variations of Eq. [9]:

d = ec × ds [9]

where d is pathogen dose−1, ec is pathogen concentration in each separate exposure scenario, and ds is dose amount as defi ned below. Soil dose was assumed at 0.480 (occupational), 0.05 (incidental), or 10 g d−1 (acute picophagia) (USEPA, 1997). Th e USEPA (1997) estimated that an adult conducting moderate outdoor activity (e.g., tending to a garden, walking along a path) would accidentally con-sume 0.05 g of dust (i.e., soil) on a daily basis, while the maximum ingestion of soil by an adult conducting rigorous activity is 0.480 g on a daily basis. Fresh vegetable (i.e., washed, without cooking such as lettuce) consumption was assumed at 0.292 kg d−1 for a 68-kg adult (USEPA, 1997).

Modifi cations made to Eq. [9] for fomite contact yielded Eq. [10]:

d = ec × fh × hm [10]

where fh is fomite-to-hand transfer rates and hm is hand-to-mouth transfer rates. Fomite-to-hand and hand-to-mouth transfers were set at 43 and 36%, respectively (Rusin et al., 2002).

Modifi cations to Eq. [9] for aerosol exposures yielded the following:

d = (ec × br × t) × ag [11]

where br is breathing rate, t is hours of exposure, and ag is aerosol ingestion rate.

Aerosolized pathogen exposures were assumed using previously defi ned exposure conditions in which br is 0.83 m3 h−1, t is 1 h d−1, and ag is 50% aerosol ingestion rate at 10 (occupational) and 100 m (indirect) downwind of the application site. Aerosol ingestion is estimated by assuming that 50% of aerosolized particles are greater than 5 μm in size (Medema et al., 2004). Inhalation transmission of enteric bacterial pathogens, which follow the fecal/oral route of infection, has not been demonstrated epidemiologically or in clinical studies, and the consideration here should be considered a worst case example.

Quantitative Microbial Risk AnalysesTh e β Poisson and one-hit exponential dose–response models

were used (Eq. [12] and [15], respectively). Dose–response parameters for each pathogen are presented in Table 1. Th e β Poisson dose–response equation was used to model the infection process for all bacterial pathogens (Haas et al., 1999):

1501 – [1 ( / ) (2 1 )]iP d N α −α= + × − [12]

where Pi is the probability of infection based on a one-time patho-gen exposure, d is the pathogen dose as calculated in Eq. [9–11], N50 is the infectivity of the pathogen, and α is a constant describ-ing the dose–response curve. In the case of C. jejuni, Medema et al. (1996) described a β value defi ned as

( )150 2 1N αβ= − [13]

Solving for N50 alters the β Poisson to Eq. [14]:

( ) 1 1iP d −α= − + β [14]

Th e one-hit exponential model was used to model adenovirus, enteroviruses, and protozoan parasitic infections (Haas et al., 1999):

1 exp( )iP rd= − − [15]

where r is the probability of a single agent capable of causing infec-tion (Haas et al., 1999). Norovirus infection was modeled using the hypergeometric function (2F1) defi ned by Teunis et al. (2008):

( )1 1 2 1( , * , , /(1 ) )i

aP F d a a a

a−

= − α +β − −

[16]

where a is the viral aggregate parameter. Specifi c dose–response models were chosen based on previous use in other QMRA stud-ies and currently accepted dose–response models (CAMRA, 2012). Th e probability for infections on an annual basis was determined using Eq. [17]:


( )ann 1 1 niP P= − − [17]

where Pann is the probability of infection based on n, the number of days or exposure events per year. Occupational annual exposures were defi ned as 255 d yr−1 (USEPA, 1997). Community exposures were defi ned as 6 d yr−1 for the aerosol exposures, and 365 d yr−1 was used for all other annual exposures. Previous work defi ned a community aerosol exposure from land application operations as 6 d, based on empirical site evaluations across the United States (Brooks et al. 2005).

Results and Discussion

Residual Pathogen LevelsTable 1 shows the lower and upper bounds of residual patho-

gen levels used in the simulations. Th e current study recognizes that in any pathogen/residual combination, the levels could be below detection or zero, which would yield a nonsignifi cant risk. Below detection levels would be typical of residual treatment, storage, or other factors (e.g., no pathogen in the population).

Th e most numerous pathogens were Salmonella spp., C. jejuni, L. monocytogenes, and norovirus with upper bounds ranging from 103 to 107 CFU or PFU g−1. Biosolids were oft en less concentrated (>2 log) than manure when both harbored the same pathogen, and always less than raw sludge. Raw municipal sludge (e.g., untreated settled sewage sludge) was included in this simulation as a means to compare nontreated agricultural residuals to nontreated munici-pal residuals, which are still used agriculturally in non-Westernized countries. Municipal residuals were the only residuals assumed to contain human pathogenic viruses.

Studies conducted across industrialized countries (United Kingdom, United States, and Australia) found bovine, poultry, and swine manure pathogen levels that varied by at least two orders of magnitude. Th is was unsurprising, given the large diff erences in AFO size (<2 × 102 to >3 × 104 animal units), type (organic vs. conventional), animal production industry (e.g., dairy, poultry, swine), and farm production system and region (e.g., swine nurs-ery vs. fi nisher farm) (USEPA, 2005; Brooks and McLaughlin, 2009; McLaughlin et al., 2009). In addition, manure storage, with no standardized process, can reduce pathogens to below detection limits (Brooks et al., 2009a). A trend toward the operation of stan-dardized CAFOs may serve to stabilize pathogen levels; however, spikes or drops in pathogen levels will always occur.

Municipal wastewater treatment plants, on the other hand, have been operating for nearly 20 yr under the guidance of the USEPA Part 503 rule (USEPA, 1993), which dictates and at least stabilizes the gross microbial quality of Class B biosolids (Viau and Peccia, 2009; Brooks et al.,2005). Salmonella and noroviruses were the pathogens found at the highest levels in biosolids (each ~103 pathogens g−1), whereas other pathogens were <101 pathogens g−1. Pepper et al. (2010) reported a decrease of Salmonella, generic E. coli, and enteroviruses, in domestic Class B biosolids from 1988 to 2006 with little regional variation, likely from full implementation of the Part 503 rule (USEPA, 1993).

Risk of Infection from Occupational

Exposures—FomitesRisk of infection from exposure to a freshly contaminated

fomite is shown in Table 2. Exposure scenarios assumed the transfer of 0.1 g of fresh residual to a fomite. Fomite exposures for each residual (assuming no decay) generally followed a trend in increasing risks: bovine < poultry = swine < biosolids < raw sludge. Exposures to raw sludge and biosolids yielded the greatest risks of 1 × 10° and 9 × 10−1, respectively, for fomite exposures without decay (Table 2). Conversely, manure residuals yielded risks no greater than 3 × 10−1. Viruses contributed to greater risk associated with municipal residuals, whereas the high infectiv-ity (i.e., N50 = 9 × 102) and a high concentration of C. jejuni in poultry and swine manure contributed to manure risk. Campy-lobacter jejuni is a microaerophilic organism and is susceptible to oxygen stress, although research has demonstrated persistence for a short period of time ( Jones et al., 1993; Stern et al., 1994; Murphy et al., 2006; Wesche et al., 2009). Th e lowest short-term risks were associated with bacteria in Class B biosolids.

Th e eff ect of fomite decay time was more pronounced for C. jejuni, Salmonella spp., and adenoviruses. When decay time was taken into account, 1 d resulted in a drop in the one-time risk by at least one order of magnitude (Table 2), and by Day 7, many were nonsignifi cant. Listeria monocytogenes and C. parvum were the least aff ected. Listeria monocytogenes risks were enhanced over all other bacterial pathogens due to a slower fomite decay rate, attributed to its thick peptidoglycan layer. When decay time (>14 d) was considered, the risks associated with residuals were driven by C. parvum as follows: poultry < swine < biosolids < raw sludge < bovine. Decay had a limited eff ect on Cryptosporidium risk due to the presence of a thick outer shell on the oocyst (Hutchison et al., 2005b); aft er 30 d, risks remained similar to a freshly con-taminated fomite. Pathogen survival on fomite surfaces may also be dependent on amount of transferred fecal residual; Boone and Gerba (2007) demonstrated viral survival on fomites as a func-tion of surface (e.g., porous) and high organic matter.

Risk of Infection from Soil Ingestion—Occupational,

Incidental, and Intentional IngestionInfectious risks were simulated for residual-contaminated soil

and subsequent exposure via occupational, incidental, and inten-tional ingestion. Table 3 shows risks from occupational soil inges-tion, following decay periods of 1 to 30 d, and incidental soil inges-tion by an adult living near an application site. In the former, swine manure (C. jejuni and L. monocytogenes) and raw sludge (norovirus and adenovirus) presented the greatest risk, followed by biosolids < poultry = bovine. Th e eff ect of decay time reduced risks for C. jejuni and adenovirus by four to fi ve orders of magnitude over the 30-d period (occupational). Th e same time period had minimal eff ect on enteroviruses. Incidental (indirect) exposures followed the same trend as occupational exposures; additionally, risks fell below 6 × 10−12 aft er 4 mo for nearly all pathogen-residual com-binations. Slower soil decay rates associated with Salmonella spp., enteric viruses, and C. parvum resulted in relatively high risks at 4 mo. Th e viral and parasitic risks are expected, given the low decay and high infectivity, whereas salmonellae have been documented to survive in soil for long periods of time (Watkins and Sleath, 1981). Th is simulation assumed a soil incorporation of residual with the


top 15 cm of surface soil to take place within 24 h. Such soil dilu-tion reduces pathogen concentrations by at least three orders of magnitude (Gale, 2005). However, delaying residual incorpora-tion beyond 24 h would likely enhance pathogen die-off , as envi-ronmental conditions (e.g., desiccation) would decrease pathogen levels with each passing day. Precipitation events could enhance and prolong pathogen survival, in turn, increasing risks (Zaleski et al., 2005). Similarly, doubling the application rate from 6 to 12 Mg ha−1 would decrease the soil dilution rate, eff ectively doubling risks (data not shown). Varying each of these factors demonstrates the complexity and specifi city of each scenario.

Table 4 shows risks of infection for a picaphagic child consum-ing 10 g of soil mixed with residual waste 7 d to 14 mo post–land application. Th e 10-g dose served to simulate a one-time acute soil exposure defi ned for a “true” picaphagic child (USEPA, 1997), which may occur in up to 19% of children (Bruhn and Pangborn, 1971). Overall, risk trends followed a similar pattern as the other soil/dust ingestion simulations, albeit with a higher initial soil dose. High doses and initially high C. jejuni manure levels kept risks high relative to biosolids within 14 d, while aft er 1 mo, enteric

viruses drove biosolids risk. USEPA Part 503 (1993) restricts public contact with “low access” biosolids-applied sites for at least 30 d postapplication of biosolids. Th e stipulations for the pica-child QMRA would violate the Part 503 rule by stipulating that the exposed would encroach on land before the 30-d wait period. Site restrictions, such as those enforced by the USEPA (1993), serve to limit these interactions. However, for persons living near these sites, fence lines can be crossed and site boundaries circum-vented, making deliberate consumption of contaminated soil plausible. Managers of high-access sites, such as parks and other public-use areas are required to keep the public off -site for at least 1 yr (USEPA, 1993), which appears adequate to reduce all residual risks. At minimum, the 1-mo restriction appears to be adequate to protect most public pathogen exposures in the current study; the exception would be the intentional (pica) exposure. No such restrictions exist for sites receiving manure.

Risk of Infection from Crop IngestionTable 5 shows fresh food crops grown on land, either contami-

nated with residual pathogens (one-time bypass and runoff ) or

Table 2. Quantitative microbial risk assessment: Occupational exposure to fomites contaminated with 0.1 g of residual waste following 0–30 d of pathogen decay, and subsequent transfer from fomite hand-mouth. Italicized and underlined residual source–pathogen combinations represent the greatest risks and greatest non–raw sludge risks for that time point, respectively.

OrganismResidual source

Risk of infection (one-time)†‡

Decay time

0 d 1 d 3 d 7 d 14 d 30 d

Low–High Low–High Low–High Low–High Low–High Low–High

Bacteria

Campylobacter jejuni

Bovine 1 × 10−2–1 × 10−1 5 × 10−5–7 × 10−4 5 × 10−12–6 × 10−11 BL§ BL BL

Poultry 4 × 10−3–3 × 10−1 2 × 10−5–1 × 10−2 2 × 10−12–8 × 10−10 BL BL BL

Swine 1 × 10−2–3 × 10−1 5 × 10−5–6 × 10−3 5 × 10−12–6 × 10−10 BL BL BL

Raw sludge 2 × 10−1–3 × 10−1 3 × 10−3–5 × 10−3 3 × 10−10–4 × 10−10 BL BL BL

Class B biosolids 5 × 10−4 2 × 10−6 2 × 10−13 BL BL BL

E. coli O157:H7 Bovine 4 × 10−3–1 × 10−1 2 × 10−5–8 × 10−4 1 × 10−12–7 × 10−11 BL BL BL

Listeria monocytogenes

Bovine 6 × 10−2–2 × 10−1 4 × 10−2–1 × 10−1 9 × 10−3–3 × 10−2 3 × 10−4–1 × 10−3 3 × 10−7–9 × 10−7 BL

Poultry 8 × 10−3–1 × 10−1 5 × 10−3–9 × 10−2 1 × 10−3−2 × 10−2 4 × 10−5–7 × 10−4 3 × 10−8–7 × 10−7 BL

Swine 3 × 10−3–3 × 10−1 2 × 10−3–2 × 10−1 4 × 10−4–7 × 10−2 1 × 10−5–3 × 10−3 2 × 10−8–3 × 10−6 BL

Raw sludge 4 × 10−2–2 × 10−1 3 × 10−2–1 × 10−1 6 × 10−3–4 × 10−2 2 × 10−4–1 × 10−3 2 × 10−7–1 × 10−6 BL

Class B biosolids 2 × 10−4–5 × 10−3 1 × 10−4–3 × 10−3 2 × 10−5–7 × 10−4 7 × 10−7–2 × 10−5 7 × 10−10–2 × 10−9 BL

Salmonella spp.

Bovine 3 × 10−4–4 × 10−3 1 × 10−6–2 × 10−5 1 × 10−13–2 × 10−12 BL BL BL

Poultry 2 × 10−6–7 × 10−3 7 × 10−9–3 × 10−5 BL–3 × 10−12 BL BL BL

Swine 1 × 10−5–7 × 10−4 5 × 10−8–3 × 10−6 4 × 10−15–3 × 10−13 BL BL BL

Raw sludge 9 × 10−4–2 × 10−2 4 × 10−6–8 × 10−5 3 × 10−13–7 × 10−12 BL BL BL

Class B biosolids 8 × 10−6–2 × 10−4 4 × 10−8–8 × 10−7 3 × 10−15–7 × 10−14 BL BL BL

Virus

AdenovirusRaw sludge 2 × 10−1–1 × 10° 1 × 10−2–2 × 10−1 3 × 10−6–6 × 10−5 1 × 10−14–2 × 10−13 BL BL

Class B biosolids 2 × 10−2–9 × 10−1 2 × 10−3–2 × 10−2 4 × 10−7–4 × 10−5 BL–2 × 10−13 BL BL

EnterovirusesRaw sludge 4 × 10−2–5 × 10−1 4 × 10−6–3 × 10−4 2 × 10−7–2 × 10−5 3 × 10−11–2 × 10−9 BL BL

Class B biosolids 1 × 10−3–4 × 10−1 1 × 10−7–1 × 10−4 5 × 10−9–6 × 10−6 6 × 10−13–8 × 10−10 BL BL

Norovirus Raw sludge 6 × 10−2–7 × 10−1 7 × 10−6–4 × 10−2 3 × 10−7–2 × 10−3 4 × 10−11–2 × 10−7 BL BL


Parasite

Cryptosporidium parvum

Bovine 2 × 10−4–1 × 10−1 2 × 10−4–1 × 10−1 2 × 10−4–1 × 10−1 2 × 10−4–1 × 10−1 2 × 10−4–1 × 10−1 2 × 10−4–1 × 10−1

Raw sludge 1 × 10−2–6 × 10−2 1 × 10−2–6 × 10−2 1 × 10−2–5 × 10−2 1 × 10−2–5 × 10−2 1 × 10−2–5 × 10−2 9 × 10−3–4 × 10−2

Class B biosolids 7 × 10−5–6 × 10−3 7 × 10−5–6 × 10−3 6 × 10−5–6 × 10−3 6 × 10−5–6 × 10−3 6 × 10−5–5 × 10−3 5 × 10−5–5 × 10−3

† Occupational risk assumes accidental contact with 0.1 g of residual contaminated on a fomite and minimal protective equipment.

‡ Occupational risk presented as one-time risks of infection.

§ BL, risks that were below reportable limits and were less than the lowest reported risk.


amended with residuals with typical harvest delays used (ideal). Th e bypass and runoff simulations should be considered as unforeseen circumvention of typical fresh food crop management practices, such as was presumed in the Salinas Valley, CA, E. coli O157:H7 outbreak of 2006 (FDA, 2006; CDHS, 2007; Jay et al., 2007).

One-time bypass scenarios resulted in high relative risks, within 7 d of soil contamination, but decreased nearly two to four orders of magnitude as a result of decay time (30 d). As with the other risk simulations, C. jejuni and viruses presented the greatest immediate risks, whereas L. monocytogenes drove manure risks at 30 d. Over-all, risks from runoff contamination of fresh food crops were rela-tively low, barring viruses, even assuming the runoff event occurred immediately following land application and 1 d before harvest. Risks associated with runoff -contaminated fresh vegetables were presumed to occur as a single yearly event (Curriero et al., 2001), although multiple seasonal events are possible (Burkholder et al., 2007). Four signifi cant runoff dilution factors were modeled, and as a result, the pathogen level per crop was signifi cantly decreased. First, it was assumed that less than 10% of the surface area of the

treated fi eld would be available for runoff since not all land would have the slope conducive to runoff . Second, it was assumed that not all land-applied pathogens would be available for runoff transport (~2%) (Brooks et al., 2012). Th ird, it was also assumed that a sig-nifi cant volume dilution (~4%) would occur while in the irrigation canal since a large volume of water would be necessary for irriga-tion given plant density and acreage (Stine et al., 2005a). Fourth and fi nally, it was assumed that pathogen–plant interaction would be limited and only minor microbial-crop attachment would occur if the crops are fl ood irrigated. However, if considering overhead spray irrigation, it is possible that a smaller volume of water would be necessary and direct contact with the leaf would occur, thus increasing attachment and risk. Even during deliberate contamina-tion, McLaughlin and Brooks (2009) predicted at most 1%, and oft en below 0.01%, Salmonella attachment rates to grass leaves, pre-sumed due to the waxy leaf surface. Th at being said, a large-scale rain event may promote microbial regrowth, or prolonged survival, particularly on residual-treated soils such as the ones simulated here (Th urston-Enriquez et al., 2005; Brooks et al., 2009a; Brooks et al.,

Table 3. Quantitative microbial risk assessment: Occupational and incidental exposure to soil contaminated with land-applied residuals, following pathogen decay time of 1–30 d and 1–14 mo, and assuming soil ingestion. Italicized and underlined residual source–pathogen combinations represent the greatest risks and greatest non–raw sludge risks for that time point, respectively.


Risk of infection†

Occupational exposure (one-time) Incidental exposure (one-time)

Decay time Decay time

1 d 7 d 30 d 1 mo 4 mo>12 mo

Low–High Low–High Low–High Low–High Low–High

Bacteria


Bovine 5 × 10−4–6 × 10−3 6 × 10−5–8 × 10−4 2 × 10−8–3 × 10−7 2 × 10−9–3 × 10−8 BL‡ BL

Poultry 2 × 10−4–7 × 10−2 2 × 10−5–1 × 10−2 8 × 10−9–4 × 10−6 8 × 10−10–4 × 10−7 BL BL

Swine 4 × 10−2–4 × 10−1 6 × 10−3–2 × 10−1 2 × 10−6–3 × 10−4 2 × 10−7–3 × 10−5 BL BL

Raw sludge 3 × 10−2–4 × 10−2 4 × 10−3–5 × 10−3 1 × 10−6–2 × 10−6 1 × 10−7–2 × 10−7 BL BL

Class B biosolids 2 × 10−5 2 × 10−6 8 × 10−10 8 × 10−11 BL BL

E. coli O157:H7 Bovine 1 × 10−7–8 × 10−3 5 × 10−5–2 × 10−3 4 × 10−7–2 × 10−5 4 × 10−8–2 × 10−6 BL BL


Bovine 3 × 10−3–1 × 10−2 7 × 10−4–2 × 10−3 4 × 10−6–1 × 10−5 4 × 10−7–1 × 10−6 BL BL

Poultry 4 × 10−4–7 × 10−3 9 × 10−5–2 × 10−3 6 × 10−7–9 × 10−6 5 × 10−8–1 × 10−6 BL BL

Swine 1 × 10−2–5 × 10−1 3 × 10−3–3 × 10−1 2 × 10−5–4 × 10−3 2 × 10−6–4 × 10−4 BL BL

Raw sludge 2 × 10−3–1 × 10−2 4 × 10−4–3 × 10−3 2 × 10−6–2 × 10−5 3 × 10−7–2 × 10−6 BL BL


Salmonella spp.

Bovine 1 × 10−5–2 × 10−4 6 × 10−6–9 × 10−5 2 × 10−7–4 × 10−6 2 × 10−8–4 × 10−7 BL–1 × 10−12 BL

Poultry 8 × 10−8–3 × 10−4 3 × 10−8–1 × 10−4 1 × 10−9–6 × 10−6 1 × 10−10–6 × 10−7 BL–2 × 10−12 BL

Swine 5 × 10−5–3 × 10−3 2 × 10−5–1 × 10−3 9 × 10−7–6 × 10−5 9 × 10−8–6 × 10−6 BL–2 × 10−11 BL

Raw sludge 4 × 10−5–8 × 10−4 2 × 10−5–4 × 10−4 7 × 10−7–2 × 10−5 7 × 10−8–2 × 10−6 BL–6 × 10−12 BL


Virus

AdenovirusRaw sludge 7 × 10−3–1 × 10−1 9 × 10−4–2 × 10−2 3 × 10−7–6 × 10−6 3 × 10−8–7 × 10−7 BL BL


EnterovirusesRaw sludge 2 × 10−3–1 × 10−1 1 × 10−3–7 × 10−2 1 × 10−4–8 × 10−3 1 × 10−5–9 × 10−4 1 × 10−9–9 × 10−8 BL


NorovirusRaw sludge 3 × 10−3–6 × 10−1 2 × 10−3–6 × 10−1 2 × 10−4–5 × 10−1 2 × 10−5–9 × 10−2 BL


Parasite


Bovine 1 × 10−5–7 × 10−3 6 × 10−6–4 × 10−3 5 × 10−7–3 × 10−4 5 × 10−8–3 × 10−5 3 × 10−12–2 × 10−9 BL

Raw sludge 6 × 10−4–3 × 10−3 3 × 10−4–1 × 10−3 2 × 10−5–1 × 10−3 2 × 10−6–1 × 10−5 1 × 10−10–6 × 10−10 BL

Class B biosolids 3 × 10−6–3 × 10−3 2 × 10−6–1 × 10−4 1 × 10−7–1 × 10−5 1 × 10−8–1 × 10−6 BL–6 × 10−11 BL

† Occupational risk assumes accidental ingestion of 480 mg of soil contaminated with land-applied residuals contaminated with pathogens over a

single work day (one-time). Incidental risk assumes dust ingestion of a 0.05-g dose of soil (one-time).

‡ BL, risks that were below reportable limits and were less than 9 × 10−12.


2012). Others have noted an increase in Salmonella found in bio-solids following rain events (Zaleski et al., 2005). An unforeseen situation such as this would be diffi cult to predict and requires more data before inputting into a QMRA model.

Th e “ideal” simulation presumed the use of residuals on lands intended for food crops. Harvest delay restrictions were assumed, as suggested by the USDA–AMS (2000), ADAS (2001), and USEPA (1993), despite the fact that neither Class B biosolids nor raw sludge are applied to food crop land; conversely, manure is occasionally used. Depending on the crop end-use and type, a delay period of 1 to 38 mo between land applications and either planting or harvesting is required. In the current ideal simulation, risks were assumed to come from consumption of fresh leafy greens on a daily basis. Risk of infection decreased rapidly at 4 mo, the exception being viruses. Assuming the 4-mo harvest delay, risks were typi-cally fi ve orders of magnitude less than the bypass scenarios (1–30 d). Annualized risks at 4 mo of decay time were below 3 × 10−4 (e.g., biosolids and manure) and at 14 mo were considered nonsig-nifi cant. Risks were simulated in defi ned ideal scenarios and did not include bypass situations such as feral animals, rainfall events pro-

moting runoff or pathogen regrowth, or human error. Th ese risks demonstrate the conservative nature of the current recommenda-tions and regulatory standards (e.g., delays of >4 mo).

Th e 2006 E. coli O157:H7 fresh spinach outbreak caused 205 reported illnesses and 5 deaths in the United States (CDHS, 2007) and can be used to validate the current risk paradigm. It was esti-mated that a 1.13-ha fi eld yielding a total of 42,000 spinach units (e.g., packages) were implicated in the outbreak; among these 29% were estimated to be positive for E. coli O157:H7 (Weise and Schmit, 2007). Using the 205 illnesses, one can estimate approxi-mately 732 infections based on probability of illness, 0.28 accord-ing to Seto et al. (2007). Seto et al. (2007) estimated that 88% of infections were a result of initial exposure to the contaminated spinach, which equates to 644 infections as a result of exposure to ~1.2 × 105 doses (4.2 × 104 spinach bags × 10 doses bag−1 × 29% positive units). Th is yields a probability of infection of 5 × 10−3. Th e current simulation, using the upper-bound E. coli O157:H7 level, yields a risk of 3 × 10−3 assuming 7-d decay during a bypass sce-nario. Th e current paradigm suggests that either concentrated fecal matter contaminated the spinach fi eld within the harvesting time-

Table 4. Quantitative microbial risk assessment: One-time intentional ingestion of soil contaminated with residual waste by a pica-child,† following 7 d- 14 mo of decay time. Italicized and underlined residual source–pathogen combinations represent the greatest risks and greatest non–raw sludge risks for that time point, respectively.

Organism Residual source

Risk of infection (one-time)

Decay time‡

7 d 14 d 1 mo 4 mo>14 mo

Low–High Low–High Low–High Low–High

Bacteria


Bovine 1 × 10−3–1 × 10−2 1 × 10−4–1 × 10−3 4 × 10−7–6 × 10−6 BL§ BL

Poultry 4 × 10−4–1 × 10−1 4 × 10−5–2 × 10−2 2 × 10−7–8 × 10−5 BL BL

Swine 9 × 10−2–5 × 10−1 1 × 10−2–3 × 10−1 4 × 10−5–5 × 10−3 BL BL

Raw sludge 6 × 10−2–8 × 10−2 7 × 10−3–1 × 10−2 3 × 10−5–4 × 10−5 BL BL

Class B biosolids 1 × 10−2–3 × 10−2 5 × 10−3–2 × 10−2 1 × 10−3–3 × 10−3 BL BL

E. coli O157:H7 Bovine 1 × 10−3–4 × 10−2 2 × 10−4–1 × 10−2 8 × 10−6–4 × 10−4 BL–3 × 10−12 BL


Bovine 2 × 10−2–5 × 10−2 3 × 10−3–1 × 10−2 8 × 10−5–3 × 10−4 BL BL

Poultry 2 × 10−3–4 × 10−2 4 × 10−4–8 × 10−3 1 × 10−5–2 × 10−4 BL BL

Swine 6 × 10−2–6 × 10−1 1 × 10−2–5 × 10−1 3 × 10−4–6 × 10−2 BL BL

Raw sludge 1 × 10−2–6 × 10−2 2 × 10−3–1 × 10−2 5 × 10−5–4 × 10−4 BL BL


Salmonella spp.

Bovine 1 × 10−4–2 × 10−3 4 × 10−5–7 × 10−4 5 × 10−6–7 × 10−5 2 × 10−11–3 × 10−10 BL

Poultry 7 × 10−7–3 × 10−3 3 × 10−7–1 × 10−3 3 × 10−8–1 × 10−4 1 × 10−13–4 × 10−10 BL

Swine 4 × 10−4–3 × 10−2 2 × 10−4–1 × 10−2 2 × 10−5–1 × 10−3 7 × 10−11–5 × 10−9 BL

Raw sludge 4 × 10−4–7 × 10−3 1 × 10−4–3 × 10−3 1 × 10−5–3 × 10−4 6 × 10−11–1 × 10−9 BL

Class B biosolids 4 × 10−6–8 × 10−5 1 × 10−6–3 × 10−5 1 × 10−7–3 × 10−6 6 × 10−13–1 × 10−11 BL

Virus

AdenovirusRaw sludge 2 × 10−2–3 × 10−1 2 × 10−3–3 × 10−2 7 × 10−6–1 × 10−4 BL BL


EnterovirusesRaw sludge 2 × 10−2–4 × 10−1 1 × 10−2–3 × 10−1 2 × 10−3–1 × 10−1 2 × 10−7–2 × 10−5 BL


NorovirusRaw sludge 4 × 10−2–6 × 10−1 2 × 10−2–6 × 10−1 4 × 10−3–6 × 10−1 4 × 10−7–2 × 10−3 BL


Parasite


Bovine 1 × 10−4–7 × 10−2 6 × 10−5–4 × 10−2 1 × 10−5–6 × 10−3 5 × 10−10–3 × 10−7 BL

Raw sludge 6 × 10−3–3 × 10−2 3 × 10−3–1 × 10−2 5 × 10−4–2 × 10−3 3 × 10−8–1 × 10−7 BL


† Pica-child: the USEPA (USEPA, 1997) defi nes a maximum one-time ingestion of a 10-g dose of soil.

‡ Pica-child risk presented as one-time risks of infection following decay time in days (7 and 14 d) and months (1–38 mo) after residual land application.

§ BL, risks that were below reportable limits and were less than the 9 × 10−12.


line or a possible increase in E. coli O157:H7 occurred as a result of regrowth (Desmarais et al., 2002; Santamaria and Toranzos, 2003; Lang et al., 2007). No evidence exists to suggest that E. coli O157:H7 can regrow in the environment, although given the leaf canopy, row irrigation, and fresh fecal matter, it is plausible. Given these conditions, it is highly probable that a small section of the fi eld was highly contaminated with fecal matter (e.g., feral animals crossing only through a small section) or harbored topographic peculiarities such as low-lying areas capable of holding water.

Risk of Infection from Aerosols—Occupational

and Public InhalationTable 6 presents risk of infection from aerosolized pathogens

generated during a residual land application event assuming favor-

able conditions for aerosolization (e.g., dry, steady wind, and high residual disturbance) (Brooks et al., 2005). Aerosolization and transport can be site specifi c (e.g., dry climate) and residual specifi c (e.g., reel guns) (Paez-Rubio et al., 2005; McLaughlin et al., 2010).

Occupational exposures were assumed to occur additively to amount to 1 h per workday and comprised an aerosol particulate ingestion rate of 50% with minimal personal protective equip-ment (Tanner et al., 2008). Exposures were assumed to occur 10 m from the source (e.g., biosolids loading) (Brooks et al., 2005; Tanner et al., 2008). Annual occupational aerosol risks (C. jejuni, L. monocytogenes, and enteric viruses) for nearly all pathogens were >10−2 (255 d yr−1), assuming the high pathogen level; in contrast, public risks were substantially lower than occupational. Public exposures comprised downwind transport (100 m), with

Table 5. Quantitative microbial risk assessment: Fresh crop ingestion† assuming bypass (one-time), runoff contamination (one-time), and ideal (annual) risk of infection either accidentally contaminated (bypass and runoff ) or grown (ideal) with residual waste following specifi c decay times. Italicized and underlined residual source–pathogen combinations represent the greatest risks and greatest non–raw sludge risks for that time point, respectively.


Risk of infection‡

Bypass (one-time) Runoff (one-time) Ideal (annual)

Decay time Decay time Decay time

7 d 30 d 1 d 1 mo 4 mo>14 mo

Low–High Low–High Low–High Low–High Low–High

Bacteria


Bovine 7 × 10−5–9 × 10−4 2 × 10−8–3 × 10−7 BL–5 × 10−13 9 × 10−6–1 × 10−4 BL§ BL

Poultry 3 × 10−5–1 × 10−2 9 × 10−9–5 × 10−6 BL–7 × 10−12 3 × 10−6–2 × 10−3 BL BL

Swine 7 × 10−3–2 × 10−1 3 × 10−6–3 × 10−4 BL–2 × 10−12 9 × 10−4–1 × 10−1 BL BL

Raw sludge 4 × 10−3–6 × 10−3 2 × 10−6–2 × 10−6 2 × 10−12–4 × 10−12 6 × 10−4–9 × 10−4 BL BL

Biosolids 3 × 10−6–3 × 10−6 1 × 10−9–1 × 10−9 BL 4 × 10−7–4 × 10−7 BL BL

E. coli O157:H7 Bovine 6 × 10−5–3 × 10−3 5 × 10−7–3 × 10−5 BL–6 × 10−13 5 × 10−7–3 × 10−5 BL BL


Bovine 4 × 10−3–1 × 10−2 5 × 10−6–2 × 10−5 6 × 10−11–2 × 10−10 2 × 10−3–6 × 10−3 BL BL

Poultry 4 × 10−4–9 × 10−3 6 × 10−7–1 × 10−5 7 × 10−12–2 × 10−10 2 × 10−4–4 × 10−3 BL BL

Swine 1 × 10−2–5 × 10−1 2 × 10−5–4 × 10−3 1 × 10−12–2 × 10−10 7 × 10−3–8 × 10−1 BL–2 × 10−9 BL

Raw sludge 2 × 10−3–2 × 10−2 3 × 10−6–2 × 10−5 4 × 10−11–3 × 10−10 1 × 10−3–8 × 10−3 BL BL

Biosolids 9 × 10−6–3 × 10−4 1 × 10−8–4 × 10−7 2 × 10−13–5 × 10−12 4 × 10−6–1 × 10−4 BL BL

Salmonella spp.

Bovine 9 × 10−6–1 × 10−4 4 × 10−7–4 × 10−6 6 × 10−11–9 × 10−10 1 × 10−4–2 × 10−3 5 × 10−10–6 × 10−9 BL

Poultry 4 × 10−8–2 × 10−4 2 × 10−9–7 × 10−6 4 × 10−13–1 × 10−9 6 × 10−7–3 × 10−3 2 × 10−12–9 × 10−9 BL

Swine 3 × 10−5–2 × 10−3 1 × 10−6–7 × 10−5 9 × 10−13–6 × 10−11 4 × 10−4–3 × 10−2 1 × 10−9–1 × 10−7 BL

Raw sludge 2 × 10−5–4 × 10−4 9 × 10−7–2 × 10−5 2 × 10−10–4 × 10−9 3 × 10−4–7 × 10−3 1 × 10−9–3 × 10−8 BL

Biosolids 2 × 10−7–4 × 10−6 9 × 10−9–2 × 10−7 2 × 10−12–4 × 10−11 3 × 10−6–7 × 10−5 1 × 10−11–2 × 10−10 BL

Virus

AdenovirusRaw sludge 1 × 10−3–2 × 10−2 4 × 10−7–8 × 10−6 1 × 10−5–2 × 10−4 2 × 10−1–1 × 10° 2 × 10−5–4 × 10−4 BL

Biosolids 1 × 10−4–2 × 10−2 5 × 10−8–6 × 10−6 1 × 10−6–2 × 10−4 3 × 10−2–1 × 10° 3 × 10−6–3 × 10−4 BL

EnterovirusesRaw sludge 1 × 10−3–8 × 10−2 1 × 10−4–1 × 10−2 6 × 10−8–4 × 10−6 5 × 10−2–1 × 10° 5 × 10−6–4 × 10−4 BL

Biosolids 3 × 10−5–4 × 10−2 3 × 10−6–4 × 10−3 5 × 10−8–6 × 10−5 1 × 10−3–7 × 10−1 1 × 10−7–1 × 10−4 BL

NorovirusRaw sludge 2 × 10−3–6 × 10−1 2 × 10−4–5 × 10−1 6 × 10−7–3 × 10−3 7 × 10−2–1 × 10° 8 × 10−6–4 × 10−2 BL


Parasite


Bovine 8 × 10−6–5 × 10−3 6 × 10−7–4 × 10−4 5 × 10−11–3 × 10−8 2 × 10−4–4 × 10−4 1 × 10−8–2 × 10−8 BL

Raw sludge 4 × 10−4–2 × 10−3 3 × 10−5–1 × 10−4 2 × 10−9–1 × 10−8 1 × 10−4–5 × 10−2 5 × 10−7–3 × 10−6 BL

Biosolids 2 × 10−6–2 × 10−4 2 × 10−7–1 × 10−5 1 × 10−11–1 × 10−9 5 × 10−5–5 × 10−3 3 × 10−9–3 × 10−7 BL

† The bypass and runoff scenarios simulated accidental deposition and rain-induced runoff of residual wastes, respectively, on and/or to lands before

harvest without regard to typical harvest delays. The ideal scenario simulated typical recommendations and rules for the time between harvest of

fresh vegetable crops and land application of residual wastes are as follows: manure—4 mo (USDA–AMS, 2000); Class B biosolids—12–30 mo depend-

ing on crop (USEPA, 1993; ADAS, 2001). These times were factored into the annualized Quantitative microbial risk assessment. Average crop ingestion

(USEPA, 1997) comprised 0.292 kg d−1.

‡ Crop ingestion risk presented as one-time and annual (365 d) risks of infection following the decay time in days or months after residual land

application.

§ BL, risks that were below reportable limits and were less than 6 × 10−13.


cumulative exposures equivalent to 1 h per day, and limited to 6 d yr−1 (Brooks et al., 2005). Low et al. (2008) demonstrated that land-application aerosol exposures may occur in an intermittent plumelike fashion, hence 1 h. As with the previous crop and soil simulations, the greatest pathogen mitigation factor was dilution as aerosolized microbial spread is Gaussian in nature (Peterson and Lighthart, 1977; Low et al., 2008).

Comparison of Manure to BiosolidsA comparison of risks demonstrated municipal residual risks

were driven by viruses, whereas manure risks were by bacteria. Raw sludge tended to harbor the greatest risk in the short and long term for most simulations. Risks from biosolids were high due to the presence of only a few viruses with higher infectivity. Compara-tively, manure risks were high due to high bacterial numbers with lower infectivity. Risks from bacteria associated with Class B bio-solids were approximately one to two orders of magnitude less than manure and raw sludge. Diff erences in infectious risk between raw sludge and biosolids were stark; raw sludge was consistently two to three orders of magnitude greater than similar biosolids risks. For

example, one-time risks from exposure to aerosolized enteroviruses (i.e., Coxsackievirus) in raw sludge ranged from 1 × 10−5 to 1 × 10−3, whereas similar exposures from Class B biosolids ranged from 3 × 10−7 to 4 × 10−4. As demonstrated by Gale (2005), eff ective treatments to reduce pathogen content from raw sludge to biosol-ids (USEPA, 1993) can considerably alter risk; the extent of risk reduction hinges on the overall treatment capability of the operat-ing treatment plant. If ignoring raw sludge, it was apparent that all residuals held high risk in the short term with the general trend as follows: bovine = poultry < swine ≤ biosolids. By 30 d, risk shift ed toward manure; aft er 30 d, however, as bacterial pathogens die off , risk shift ed back toward biosolids, as a function of a few viral par-ticles with high infectivity. When directly compared with shared pathogens, manure risks were generally greater than biosolids risks.

Risk Reduction as a Function of Time and DilutionInfectious risks can be dramatically aff ected by microbial decay

time and/or environmental dilution as demonstrated herein with fomite, soil, crop, and aerosol exposures. Interestingly, risks behaved as a function of residual and time, as demonstrated above, with over-

Table 6. Quantitative microbial risk assessment: Occupational and indirect public exposure and subsequent annualized risks from inhalation of aerosols transported during the land application of residuals. Italicized and underlined residual source–pathogen combinations represent the greatest risks and greatest non–raw sludge risks for that time point, respectively.

Organism†Residual source

Risk of infection‡§

Occupational Public

One-time Annual Annual

Low–High Low–High Low–High

Bacteria


Bovine 2 × 10−4–2 × 10−3 4 × 10−2–4 × 10−1 5 × 10−7–7 × 10−6

Poultry 6 × 10−5–3 × 10−2 2 × 10−2–1 × 10° 2 × 10−7–1 × 10−4

Swine 2 × 10−4–3 × 10−2 4 × 10−2–1 × 10° 5 × 10−7–7 × 10−5

Raw sludge 1 × 10−2–1 × 10−2 9 × 10−1–1 × 10° 3 × 10−5–5 × 10−5

Biosolids 6 × 10−6 2 × 10−3 2 × 10−8

E. coli O157:H7 Bovine 5 × 10−5–3 × 10−3 1 × 10−2–5 × 10−1 2 × 10−7–8 × 10−6


Bovine 9 × 10−4–3 × 10−3 2 × 10−1–5 × 10−1 3 × 10−6–1 × 10−5

Poultry 1 × 10−4–2 × 10−3 3 × 10−2–5 × 10−1 4 × 10−7–8 × 10−6

Swine 4 × 10−5–9 × 10−3 1 × 10−2–9 × 10−1 1 × 10−7–3 × 10−5

Raw sludge 6 × 10−4–4 × 10−3 1 × 10−1–7 × 10−1 2 × 10−6–1 × 10−5

Biosolids 2 × 10−6–7 × 10−5 6 × 10−4–2 × 10−2 8 × 10−9–2 × 10−7

Salmonella spp.

Bovine 4 × 10−6–6 × 10−5 9 × 10−4–1 × 10−2 1 × 10−8–2 × 10−7

Poultry 2 × 10−8–9 × 10−5 6 × 10−6–2 × 10−2 7 × 10−11–3 × 10−7

Swine 1 × 10−7–9 × 10−6 4 × 10−5–2 × 10−3 4 × 10−10–3 × 10−8

Raw sludge 1 × 10−5–2 × 10−4 3 × 10−3–6 × 10−2 4 × 10−8–8 × 10−7

Biosolids 1 × 10−7–2 × 10−6 3 × 10−5–6 × 10−4 4 × 10−10–8 × 10−9

Virus

AdenovirusRaw sludge 3 × 10−3–5 × 10−2 5 × 10−1–1 × 10° 8 × 10−6–2 × 10−4

Biosolids 3 × 10−4–4 × 10−2 8 × 10−2–1 × 10° 1 × 10−6–1 × 10−4

EnterovirusesRaw sludge 1 × 10−5–1 × 10−3 4 × 10−3–2 × 10−1 5 × 10−8–3 × 10−6

Biosolids 3 × 10−7–4 × 10−4 8 × 10−5–1 × 10−1 1 × 10−9–1 × 10−6

NorovirusRaw sludge 9 × 10−4–6 × 10−1 2 × 10−1–1 × 10° 3 × 10−6–2 × 10−2

Class B biosolids 3 × 10−4–8 × 10−4 6 × 10−2–2 × 10−1 8 × 10−7–2 × 10−6

† Cryptosporidium parvum aerosol risks were not considered.

‡ Occupational risk assumes inhalation (1 h) of aerosol particulates contaminated with land-applied residuals contaminated with pathogens over a

single work day.

§ Indirect inhalation (1 h) risk of aerosol particulates contaminated with land-applied residuals contaminated with pathogens over a typical 6-d

application cycle; exposures were assumed to occur 100 m downwind of the application site. Occupational risk presented as one-time risks of

infection and annual risks of infection (255 d yr−1).


all risk dynamic as a function of pathogen decay. Risk from expo-sure to pathogen-contaminated fomites was signifi cantly reduced by even 1 d of decay (greater than three orders of magnitude). Risk from accidental exposure to contaminated soil decreased by at least one order of magnitude when decay times were increased from 1 to 7 d. In this case, the rate of soil decay was consistently smaller than fomite surface decay, due to protection off ered by soil (Heinonen-Tanski and Uusi-Kamppa, 2001). Initial soil and aerosol dilutions reduced infectious risks by at least two orders of magnitude com-pared with the more direct fomite exposure scenarios. Fomite expo-sure is one uniquely faced by the occupationally exposed.

Comparison of RisksComparison among occupational and public risks is inherently

diffi cult; there is no standard or suggested risk (Regli et al., 1991) with which to compare. Th e public risks simulated herein cover a wide range of pathogen exposures involving various residual waste land-application scenarios and exposures that occur indirectly, such as fresh food crop consumption, soil dust ingestion, and downwind aerosol inhalation. Th e greatest risks associated with public expo-sures resulted from intentional soil consumption (e.g., picaphagic child) with limited decay, whereas the greatest occupational risks were from fomite exposures (e.g., residual-contaminated handle). Both scenarios’ high risk was due to limited microbial decay and more direct contact with the residual. A deliberate visitation and consumption (pica) could be considered an intentional (i.e., self-imposed) risk, with the exposure occurring due to disregard for signage, whereas the occupational exposure may occur as a result of a normal workday. Both risks would be preventable, but some occupational risk is inherent to the job. Removing self-imposed risk signifi cantly reduces public risks.

Risks common to occupational and public exposures included soil consumption and aerosol inhalation. As expected, occupa-tional exposures resulted in more infectious risk when compared to public, oft en orders of magnitude greater. Occupational exposures would typically occur almost daily with residual or within days of handling fresh residual, although residual quality will vary through-out that time. Although they may occur daily, public exposures (e.g., as dust ingestion), would be expected to occur days or months aft er land application. Public risks would be limited by environmental pathogen inactivation, particularly assuming regulations are fol-lowed (USEPA, 1993; USDA–AMS, 2000; ADAS, 2001).

Only a few other studies have described public exposures to residual wastes (Dowd et al., 2000; Gerba et al., 2002; Engle-hardt et al., 2003; Brooks et al., 2005; Gale, 2005; Eisenberg et al., 2004, 2008; Viau et al., 2011). Gale (2005) predicted risks from consumption of fresh food crops to be no greater than 4 × 10−5 yr−1(Giardia), without considering a longer harvest delay (12 to 30 mo). Similarly, risks from Salmonella were predicted to be approxi-mately 8 × 10−9 yr−1 (Gale, 2005). Even using these conservative assumptions, the risks would be approximately one infection in the United Kingdom every 111 yr as a result of the land application practice (Gale, 2005). Conversely, when extrapolating to harvest delay times, the risk of infection per year from consuming Salmo-nella-contaminated food crops was calculated to be <10−14 and from enteroviruses to be <10−12 (Gale, 2005). Th ese risks are similar to those in the current simulation, given comparable scenarios and delays. Eisenberg et al. (2008) refi ned earlier risk models to estab-lish a framework for biosolids risk modeling using direct ingestion,

groundwater ingestion, aerosol inhalation, and secondary transmis-sions. In the Eisenberg et al. (2008) study, modeling exposure to rotavirus, the authors calculated a mean risk of 1 × 10−3 for con-sumption of 100 mg of single digested biosolids, and 2 × 10−2 for consumption of groundwater under karst soil conditions. Simply assuming some level of soil fi ltration (i.e., through 30 m) reduced these risks to 6 × 10−9, demonstrating environmental attenuation. Other previous public exposure risks involved aerosol inhalation or ingestion. In the present study, public annual risks of aerosol inhala-tion ranged from 2 × 10−11 to 8 × 10−4 at 100 m downwind of the application site. Previously, similar risks were calculated and ranged from 1 × 10−11 to 1 × 10−6 (Brooks et al., 2005). Viau et al. (2011) calculated aerosol annual risks from Salmonella, Coxsackievirus, norovirus, and adenovirus ranging from 10−10 for Salmonella to 10−1 for norovirus (based on genomic units), with median values comparable to the current study. Th ese risks were calculated by esti-mating downwind pathogen concentrations using a novel method that combined biosolids-based PM10 and molecular-based patho-gen levels (i.e., qPCR).

Occupational risk studies rarely account for microbial risks. Notable exceptions include risks to healthcare personnel, wastewa-ter treatment plant operators, and CAFO workers (Medema et al., 2004; Westrell et al., 2004; Price et al., 2007; Tanner et al., 2008). Notably, Medema et al. (2004) predicted annual risks from approx-imately 2 × 10−1 to a high of 1 from exposure to enteric pathogens in aerosols at wastewater treatment plants. Tanner et al. (2008) simulated risks from aerosols that ranged from less than 2 × 10−2 per year (i.e., moderate exposures and use of personnel protective equipment) to 3 × 10−1 (i.e., no protective equipment). Th e current study predicted comparable aerosolized risks.

Th e simulated occupational exposures are probably infre-quent but characterize exposure to a select subset of the working population. Th e working-year exposure duration (255 d, USEPA 1997) undoubtedly overexpresses risk, as handling a contaminated fomite, ingesting soil, or inhaling aerosols on a daily basis (using the same conditions and concentrations) is unlikely. Frequently, han-dlers may receive a shipment of residual that is not land-applied until 5 to 7 d later, thus changing the microbial load inherent to the residual and subsequently creating a unique risk each passing day (e.g., lower or higher depending on climate and storage). Likewise, occupationally exposed personnel represent populations uniquely immunologically desensitized to some of these enteric pathogens (Medema et al., 2004). Operators reporting to duty for the fi rst time are more likely to experience symptoms of infection, but the symptoms dissipate aft er prolonged employment (Medema et al., 2004). A Monte Carlo simulation (Westrell et al., 2004; Gale, 2005) could be used to simulate all the various scenarios and levels of health but was deemed beyond the scope of the present study. Th e simulations involving soil or fomite contamination, reported in the present study, are the fi rst of their kind and have no contem-porary microbial comparisons in the literature.

LimitationsInherently, risk simulations have sources of uncertainty, such

as pathogen levels, dose–response models, environmental condi-tions, exposures, end point doses, and population types. Careful data interpretation is necessary before undertaking a literature-based QMRA and, likewise, caveat emptor must be carefully pre-sented with each analysis. Th ese errors can be tested by running a


series of sensitivity analyses, in which each variable can be altered to determine its eff ect on the fi nal risk assessment. For instance, varying pathogen level by one order of magnitude could alter risks by more than one order of magnitude (data not shown). Data col-lection represents one of the largest infl uences on risk analyses. A case in point is the use of genomic units compared with culturable units in risk analyses. Th e advent of qPCR has allowed researchers to quantify previously uncultivable pathogen targets, such as nor-ovirus; however, the presence of the target nucleic acid does not necessarily equate to the presence of an infectious unit. In a study by Wong et al. (2010), the presence of enteroviruses based on cul-ture assays in Buff alo green monkey (BGM) and A549 cell lines was 0.725 and 120 most probable number (MPN) g−1, respec-tively, whereas approximately 104 GU g−1 were obtained via qPCR. Understanding these discrepancies is paramount in future risk analyses; for example, on the one hand, exclusive use of values gen-erated by growth in BGM cells underestimates the risk, whereas, on the other, exclusive use of the genomic values overestimates. Treatment effi ciency, infl uent raw waste quality, and variability in waste types increase these uncertainties; however, Pepper et al. (2010) and Viau and Peccia (2009) suggest that microbial qual-ity within United States’ Class B biosolids falls within defi nable limits. Conversely, manure pathogen quality is largely unknown and variable (Pell, 1997; Hutchison et al., 2005a; McLaughlin et al., 2009). Th erefore, it is critical that the risk framework account for these discrepancies. One such approach is to use a wide range of reported values including the lower and upper pathogen levels, essentially encompassing the range of mean pathogen levels expected for a given source. Th is approach has been applied in residual risk analyses by Brooks et al. (2005, 2009b), Tanner et al. (2008), ASM (2011), and the current study. A Monte Carlo analy-sis would accomplish this as well (Viau et al., 2011).

It is also diffi cult to simulate the behavior of an enteric biologi-cal system in the environment, as inactivation rates vary by species, subspecies (Bolster et al., 2010), waste types, and soil types. In the current analyses, for example, the application of inactivation rates across all waste types increased uncertainty, as not all waste types behave the same. For instance, it could be expected that greater organic matter would equate to less inactivation, which also applies to soil types and agronomic systems. In similar fashion, the Pois-son distributive nature of microbial pathogens in soil and in the environment can act in two contrary ways; one increases the risk associated with a specifi c particle of soil to which the pathogen is attached, while simultaneously decreasing the risk associated with the remaining pathogen-free particles (Gale, 2005). Th e use of Monte Carlo simulations can estimate this variability (Westrell et al., 2004), although applying a range of values for a given variable in the risk model adequately addresses many of these shortcomings, as eloquently stated by Gale (2005).

ConclusionsTh is simulation involved the fi rst ever comparison of risks from

manure and biosolids using real-world pathogen levels, empirically derived environmental pathogen inactivation, and environmental exposures. Th e overarching trend suggests that residual/pathogen risks were highly dynamic over time, with high risks associated with all residuals immediately at application, bacterial and parasitic risks high up until 30 d, and relatively high viral risks associated with only a few viral particles beyond that time. Time and dilution were

the mitigating factors in reducing all risks. Many of the immediate risks inherent to these practices are largely unavoidable (i.e., occu-pational), whereas some risks and interactions can be limited and prevented (e.g., pica, circumventing site boundaries). Th is study suggests that the use of waste residuals on lands intended for food crops should be revisited, as most risks were low by 4 mo and below signifi cance at 12 mo of harvest delay. In addition, residual pre-application handling (e.g., storage, anaerobic digestion) signifi cantly reduces pathogen loads, as evidenced by overall low pathogen levels in both manure and biosolids. Nonetheless, the risks presented here represent specifi c exposure scenarios for which only a few variables were manipulated; unforeseen extreme situations such as large-scale rain events, regrowth, high virulence and environmental recalci-trance, high-dose exposures, and wildlife represent factors that are diffi cult to predict. Although many studies have been conducted, predicting the behavior of a biological system, such as a zoonotic pathogen in the environment, remains poorly understood, as evi-denced by the many foodborne outbreaks of recent times. Factors such as pathogen levels, eff ect of storage and/or treatment, environ-mental inactivation, exposure frequency and duration, and popula-tions are poorly understood and contribute to uncertainty. Given the low pathogen levels in these residuals, the key to understanding and predicting risk appears to be in predicting what the environ-mentally recalcitrant single viral, bacterial, or parasitic particle or cell will do while in the environment. In some instances, future data collection, using molecular approaches, needs further clarifi cation for risk analyses but can be used for many of these questions. Future research endeavors should focus on addressing these issues.

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