Scholarly article on topic 'Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock – A review to inform risk assessment studies'

Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock – A review to inform risk assessment studies Academic research paper on "Environmental engineering"

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Abstract of research paper on Environmental engineering, author of scientific article — J.P.G. Van Leuken, A.N. Swart, A.H. Havelaar, A. Van Pul, W. Van der Hoek, et al.

Abstract In this review we discuss studies that applied atmospheric dispersion models (ADM) to bioaerosols that are pathogenic to humans and livestock in the context of risk assessment studies. Traditionally, ADMs have been developed to describe the atmospheric transport of chemical pollutants, radioactive matter, dust, and particulate matter. However, they have also enabled researchers to simulate bioaerosol dispersion. To inform risk assessment, the aims of this review were fourfold, namely (1) to describe the most important physical processes related to ADMs and pathogen transport, (2) to discuss studies that focused on the application of ADMs to pathogenic bioaerosols, (3) to discuss emission and inactivation rate parameterisations, and (4) to discuss methods for conversion of concentrations to infection probabilities (concerning quantitative microbial risk assessment). The studies included human, livestock, and industrial sources. Important factors for dispersion included wind speed, atmospheric stability, topographic effects, and deposition. Inactivation was mainly governed by humidity, temperature, and ultraviolet radiation. A majority of the reviewed studies, however, lacked quantitative analyses and application of full quantitative microbial risk assessments (QMRA). Qualitative conclusions based on geographical dispersion maps and threshold doses were encountered frequently. Thus, to improve risk assessment for future outbreaks and releases, we recommended determining well-quantified emission and inactivation rates and applying dosimetry and dose–response models to estimate infection probabilities in the population at risk.

Academic research paper on topic "Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock – A review to inform risk assessment studies"

Microbial Risk Analysis 000 (2015) 1-21

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Microbial Risk Analysis

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MICROBIAL

RISK ANALYSIS

Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock - A review to inform risk assessment studies

J.P.G. Van Leukena'b'*, A.N. Swarta, A.H. Havelaar^, A. Van Puld, W. Van der Hoeka, D. Heederikb

a Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands b Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands c Emerging Pathogens Institute and Animal Sciences Department, University of Florida, Gainesville, FL, United States of America d Environment & Safety (M&V), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands

ARTICLE INFO

ABSTRACT

Article history: Received 19 May 2015 Revised 25 June 2015 Accepted 17 July 2015 Available online xxx

Keywords:

Airborne

Pathogens

Respiratory infections

Transmission

Simulation

In this review we discuss studies that applied atmospheric dispersion models (ADM) to bioaerosols that are pathogenic to humans and livestock in the context of risk assessment studies. Traditionally, ADMs have been developed to describe the atmospheric transport of chemical pollutants, radioactive matter, dust, and particulate matter. However, they have also enabled researchers to simulate bioaerosol dispersion.

To inform risk assessment, the aims of this review were fourfold, namely (1) to describe the most important physical processes related to ADMs and pathogen transport, (2) to discuss studies that focused on the application of ADMs to pathogenic bioaerosols, (3) to discuss emission and inactivation rate parameter-isations, and (4) to discuss methods for conversion of concentrations to infection probabilities (concerning quantitative microbial risk assessment).

The studies included human, livestock, and industrial sources. Important factors for dispersion included wind speed, atmospheric stability, topographic effects, and deposition. Inactivation was mainly governed by humidity, temperature, and ultraviolet radiation.

A majority of the reviewed studies, however, lacked quantitative analyses and application of full quantitative microbial risk assessments (QMRA). Qualitative conclusions based on geographical dispersion maps and threshold doses were encountered frequently. Thus, to improve risk assessment for future outbreaks and releases, we recommended determining well-quantified emission and inactivation rates and applying dosimetry and dose-response models to estimate infection probabilities in the population at risk.

© 2015 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

1.1. Perspective on bioaerosols

Aerobiology is the research area focusing on the generation and transport of bioaerosols. Bioaerosols are small, airborne particles consisting of biological material (from bacteria, viruses, spores, fungi, algae, protozoa, and pollen) either attached to particulate matter or not (Bovallius and Roffey, 1987; Després et al., 2012; Dungan, 2010; Gilbert and Duchaine, 2009; Griffin, 2007; Stark, 1999; Wéry, 2014). Large amounts of bioaerosols are produced each year by sources in-

* Corresponding author: Centre for Infectious Disease Control, National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands. Tel.:+31 3 0 274 2003.

E-mail address: jeroen.van.leuken@rivm.nl (J.P.G. Van Leuken).

eluding the natural environment and livestock farms (Cambra-Lôpez et al., 2010; Viana et al., 2008).

Pathogenic or infectious bioaerosols possibly cause respiratory infections after penetration into the respiratory system of humans or animals (Stärk, 1999; Stuart and Wilkening, 2005; Wéry, 2014). The pathogenicity to cause disease is dependent on the pathogen's in-fectivity, and its ability to be transported and to survive (Anderson and Bokor, 2012; Kersh et al., 2013; La Scola and Raoult, 2001; Rous-set et al., 2009). After being emitted or aerosolised from its source, dispersion to the surrounding environment (nearby residents, livestock, etc.) may occur. However, large-scale measurements are not generally available, time-consuming and expensive, thus complicating pathogen quantification. Also, pathogens may be inactivated in the air as well (e.g., by temperature or humidity), (Després et al., 2012; Griffiths and DeCosemo, 1994; Verreault et al., 2008).

http://dx.doi.org/10.1016/j.mran.2015.07.002

2352-3522/© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

2 J.P.G. Van Leuken et al. /Microbial Risk Analysis 000 (2015) 1-21

1.2. Atmospheric dispersion models

Atmospheric dispersion models (ADMs) may be helpful to describe the dispersion of pathogenic bioaerosols. ADMs are mechanistic models describing the transport of gases and particles - including chemical pollutants, radioactive matter, particulate matter, and dust - in the atmosphere in space and time (Holmes and Morawska, 2006; Markiewicz, 2012; Potempski et al., 2008). ADMs are widely used in the risk assessment of hazardous effects of air pollution on humans and the environment (e.g., Schaap et al., 2013). Sources are classified as either continuous (e.g., air and odour quality monitoring of emissions from industry or animal housing) or instantaneous (e.g., release of hazardous material from large fires in industrial buildings).

The advantage of mechanistic models is that they incorporate physical processes describing dispersion and that they are able to predict the dispersion process based on measurements (Kuparinen, 2006). Furthermore, most measurements are point samples in space and/or time, but ADMs can predict concentrations at high spatial and temporal resolutions. During an outbreak they may efficiently provide information, either to inform sampling or for the benefit of other response functions, such as vaccination or distribution of antibiotics (Stuart and Wilkening, 2005).

Historically, ADMs were often based on the Gaussian dispersion equation (see Section 2.4; Markiewicz, 2012, Millner, 2009) to calculate concentrations at local scales (<30 km) in a three-dimensional frame. Nowadays, most ADMs include important atmospheric processes related to fluid dynamics (e.g., turbulence) as well (Nathan et al., 2005; Upper and Hirano, 1991). Some also simulate trajectories of backward and forward spatial motions, or dispersion of so-called pollutant puffs. Furthermore, increased computer power has stimulated the development of models based on computational fluid dynamics (CFD) that include landscape features such as buildings and trees (Nathan et al., 2005; Westbrook and Isard, 1999).

Although ADMs were initially developed to simulate chemical pollutant dispersion, they have enabled researchers to simulate dispersion of bioaerosols at different spatial and temporal scales and resolutions (Després et al., 2012). Moreover, by using quantitative estimates of emission rates (i.e. the amount of pathogen emitted per unit of time, see Section 2.2), airborne concentrations (representing exposure) can be converted to doses using dosimetry models (see Section 3) to subsequently perform a quantitative microbial risk assessment (QMRA) (Paez-Rubio et al., 2007; Upper and Hirano, 1991).

ADMs are useful to address concerns about public health risks related to exposure from, for instance, livestock sources and sources related to biosafety agents (e.g., Bacillus anthracis and Coxiella burnetii) (Anderson and Bokor, 2012; Smit et al., 2012). In addition, ADMs are particularly useful in case of future outbreaks or releases. Knowledge of pathogen emission, host-susceptibility, and complex atmospheric processes may help professionals to assess and to reduce airborne infection risks (Westbrook and Isard, 1999).

1.3. Aim and outline

The objectives of this review were to present an overview of:

• the most important physical processes related to atmospheric dispersion modelling and pathogen transport (Section 2),

• studies that focused on the application of ADMs to simulate airborne transmission of pathogenic bioaerosols (Section 4),

• parameterisations regarding emission and inactivation in these ADM studies (Section 5), and

• methods for conversion of concentrations to infection probabilities applied (concerning quantitative microbial risk assessment) in the ADM studies (Sections 3 and 6),

and to place these in the context of risk assessment modelling. We focused on pathogenic bioaerosols transmitted in the outdoor envi-

Table 1

List of abbreviations.

Pathogens

AIV Avian influenza virus

FMDV Foot-and-mouth-disease virus

PRV Pseudorabies virus

SARS Severe Acute Respiratory Syndrome

Atmospheric dispersion models

ADMS Atmospheric Dispersion Modelling System

AERMOD AMS/EPA Regulatory Model

ALOHA Areal Locations of Hazardous Atmospheres

CALPUFF Californian Puff model

DERMA Danish Emergency Response Model of the Atmosphere

DREAM Dust Regional Atmospheric Model

GIADA Guida Interattiva ad Applicazione per la Dispersione Atmosferica

HPAC Hazard Prediction and Assessment Capability

HYSPLIT Hybrid Single-Particle Lagrangian Integrated Trajectory model

INPUFF Integrated PUFF model

LODI Lagrangian Operational Dispersion Integrator

MLCD Modèle Lagrangien Courte Distance

NAME Numerical Atmospheric-dispersion Modelling Environment

OMEGA Operational Multiscale Environment Model with Grid Adaptivity

OPS-ST Operational Priority Substances Short Term model

RIMPUFF Riso Mesoscale PUFF model

Meteorological models

ECMWF European Centre for Medium-Range Weather Forecasts

HiRLAM High Resolution Limited Area Model

LAPS Limited Area Prediction System (ABM)

MM5 Fifth-generation Penn State/NCAR Mesoscale Model

NCEP/NCAR Numerical Weather Prediction model of NCEPand NCAR

Institutes

ABM Australian Bureau of Meteorology (Australia)

AMS American Meteorological Society (USA)

DWD German Weather Service (Deutsche Wetter Dienst) (Germany)

EPA Environmental Protection Agency (USA)

KMAA Korean Meteorological Administration Agency (South-Korea)

KNMI Royal Netherlands Meteorological Institute (The Netherlands)

NCAR National Center for Atmospheric Research (USA)

NCEP National Centers for Environmental Prediction (USA)

NMI Norwegian Meteorological Institute (Norway)

NOAA National Oceanic and Atmospheric Administration (USA)

CFD Computational Fluid Dynamics

CFU Colony forming units

DR Dose-response

GDAS Global Data Assimilation System

ID50 Median infectious dose

IU Infectious unit

LD50 Median lethal dose

NWP Numerical Weather Prediction (model)

PSD Particle size distribution

QMRA Quantitative microbial risk assessment

SIR Susceptible-Infected-Recovered

TCID50 Median tissue culture infectious dose

WWTP Wastewater treatment plant

ronment causing airborne infections in humans and livestock. Our focus was not on direct human-human or animal-animal transmission. We used the word pathogen in the context of pathogenic bioaerosols. Tables 1 and 2 list respectively all abbreviations and parameters used in this review. Table 3 lists all atmospheric dispersion models discussed in this review. Appendix A lists all studies reviewed in Section 4.

2. Atmospheric dispersion models

2.1. Physical processes

Five major processes are related to the number of infections caused by airborne pathogens:

(1) The amount of pathogen released per unit of time (emission rate), being a function of pathogen availability and the aerosolisation rate (Shao, 2008; Viana et al., 2008; see Section 2.2).

J.P.G. Van Leukenetal./Microbial Risk Analysis 000 (2015) 1-21

Table 2

List of parameters discussed in this review.

Parameter Explanation Unit Equation(s)

a Age [years] (10), (11)

A Cross-sectional area [m2] (3),(4)

Cl, C2 Shape parameters [m3 years g-1] (11)

C Concentration [gm3] (1)-(5)

D Mean pathogen dose [gm3] (7)-(12)

h Plume height [m] (5)

H Emission height [m] (2)

Kx Eddy diffusion [m2 s-1] (1)

coefficient in x direction

Ky Eddy diffusion [m2 s-1] (1)

coefficient in y direction

Kz Eddy diffusion [m2 s-1] (1)

coefficient in z direction

n Number of pathogens [#] (6)

Pinf Probability of infection [dimensionless] (6)-(12)

Q Emission rate [gs-1] (1)-(5)

r Single-hit probability [dimensionless] (6), (7)

t Time [s] (1), (5)

u Wind speed in the x [ms-1] (1)

direction

U Wind speed in the [ms-1] (2), (3), (5)

downwind direction of a

source

v Wind speed in the y [ms-1] (1)

direction

w Wind speed in the z [ms-1] (1)

direction

W Width ofthe plume [m] (5)

column

x Coordinate [m] (1), (2)

y Coordinate [m] (1), (2)

z Coordinate [m] (1), (2)

Z0 Roughness length [m] (5)

a Parameter in the [dimensionless] (8), (9)

hyper-geometric and

Poisson dose-response

models

в Parameter in the [dimensionless] (8), (9)

hyper-geometric and

Poisson dose-response

models

Y Shape parameter [dimensionless] (10)

Л Shape parameter [dimensionless] (10)

6 Shape parameter [dimensionless] (10)

? Shape parameter [dimensionless] (10)

п Shape parameter [dimensionless] (12)

в Angle between wind [deg] (5)

direction and field edge

к Shape parameter [dimensionless] (12)

Л Inactivation rate [s-1] (2)

a y(x) Diffusion factor in y [m] (2)

direction

a z(x) Diffusion factor in z [m] (2)

direction

Ф Flow rate [m3 s-1] (4)

(2) Meteorological effects, such as wind speed, wind direction, turbulence, and deposition (Stull, 2000). Mechanical turbulence is generated by wind speed variation in height; convec-tive turbulence is related to the stability of the atmosphere.

A mix of different wind conditions and solar radiation results into three basic states of the atmosphere - unstable, stable, and neutral - that largely influence the surface layer concentrations (Jacob, 1999). Unstable atmosphere are characterised by vertical (buoyant) motions induced by thermal convection that lifts particles up to higher altitudes, thus leading to decreased surface concentrations. A stable atmosphere leads to higher surface concentrations as low wind speeds and limited solar radiation limit vertical mixing. Neutral atmospheres are characterised by mainly horizontal (advective) turbulent motions with moderate vertical mixing and high horizontal plume extent.

Deposition is subdivided in wet deposition (the removal of particles by cloud and rain droplets) and dry deposition (the dust flux from the atmosphere to the surface through molecular and turbulent diffusion and gravitational settling) (Flossmann et al., 1985; Petroff et al., 2008; Shao, 2008). The dry deposition rate is a function of particle size: very large particles (±70 ^m) deposit about 10,000 times faster than ultrafine (<0.1 ^m) particles (Lin et al., 1994). The particle size distribution - the relative amount of particles as a function of mass or number - is therefore an important predictor for the distance covered (Blatnyet al., 2011).

(3) Inactivation (Griffin, 2007), expressed as a function of time or meteorological conditions, such as temperature and humidity (Zhao et al., 2014; see Section 2.5). Large differences in inactivation rates are observed among bioaerosols (Zhao et al., 2014; see Section 5.2): viruses and vegetative bacteria maybe inactivated within minutes to hours or days, while spores are generally highly persistent (Dungan, 2010; Griffiths and DeCosemo, 1994; Jones and Harrison, 2004; Stuart and Wilkening, 2005). Note that growth of microorganisms can also occur (Harrison et al., 2005).

(4) The amount of pathogens inhaled, with breathing rate, lung volume, and particle size being important factors (Després et al., 2012; Rostami, 2009; Wilkinson et al., 2012; see Section 3). With respect to particle size, the inhalable (<100 ^m: particles breathed in), thoracic (<10 ^m: particles entering the lung's airways), and respirable fraction (<5 ^m: particles penetrating the terminal bronchioles) are distinguished (Millner, 2009).

(5) The host's health response as a function of inhaled dose (Teunis and Havelaar, 2000; see Section 3).

Thus, a full risk assessment would comprise the chain of emission quantification, atmospheric dispersion modelling, dose estimation, and estimating the probability of infection using dose-response models (Section 3).

To run ADMs, meteorological data (observed, modelled, or predicted) are required. Observational data comprise in situ measurements, data from (local) weather stations, or data from the Global Data Assimilation System (GDAS), which is a worldwide weather observation database (NOAA 2014). Modelled data are generally retrieved from numerical weather prediction models using meteorological observations from GDAS. For local/regional dispersion studies (up to several kilometres) observational data from a nearby meteorological station are sufficient or high-resolution weather data (2.5 km) could be used (Van der Plas et al., 2012). For very local dispersion studies, where the effect of local landscape features is relevant, in situ measurements are required describing the local micrometeorology.

2.2. Emission rates

An emission rate is defined as an amount released per unit of time. Emission rates for pathogens depend on source type (pigs, poultry, industrial, humans, etc.), source characteristics (e.g., stable construction or animal activity), excretion route (e.g., exhaled air or faeces), pathogen species or strain, particle size, etcetera. For a full quantitative risk assessment, quantified emission rates are required.

2.3. Eulerian and Lagrangian ADMs

ADMs are either Gaussian (Section 2.4) or Eulerian or Lagrangian. The Eulerian model is based on a fixed grid in space where the concentration as a function of time is described for an observer at a specific location. The vertical dimension (z) is generally expressed as height [m] or pressure [Pa]. Most Eulerian models are based on the advection-diffusion equation, being a simplification of the more

J.P.G. Van Leuken et al. /Microbial Risk Analysis 000 (2015) 1-21

Table 3

Atmospheric dispersion models discussed in this review (* = unknown).

Abbreviation Developer Gaussian plume Eulerian Lagrangian Depositiona PSDb Reference

Advection-diffusion CFD Gaussian puff Particle mode Trajectory

ADMS Cambridge Environmental Research Consultants, Met Office, INNOGY Holdings plc, University of Surrey (UK) x d, w yes (Carruthers et al., 1994)

AERMOD AMS (USA); EPA (USA) x d, w yes (Cimorelli et al., 2004; EPA, 2004)

ALOHA NOAA (USA) x - - - - no (Jones et al., 2013)

CALPUFF EPA (USA) - - - x - d, w yes (Scire et al., 2000)

DERMA Danish Meteorological Institute (Denmark) - - - x - d, w yes (S0rensen et al., 2007)

DREAM University of Malta - x - - d, w yes (Nickovic et al., 2001)

Fluent ANSYS (USA) - - x - - * * (ANSYS, 2014)

GIADA Italian Environmental Protection Agency x - - - * * *

HPAC Defense Threat Reduction Agency (USA) - - - x - * * *

HYSPLIT NOAA (USA); ABM (Australia) - - - x x x d, w yes (Draxler and Hess, 2014)

ICAIR * - - - x - * * *

INPUFF EPA (USA) - - - x - d yes (Petersen and Lavdas, 1986)

LODI Department of Energy (USA), University of California (USA) x d, w yes (Leone et al., 2001)

MLCD University of Alberta (Canada); Canadian Meteorological Centre (Canada) - x d, w no (Flesch et al., 2002)

NAME Met Office (UK) - x xx d, w yes (Borrego and Norman, 2007, chapter 62)

OMEGA Center for Atmospheric Physics, Science Applications International Corporation (USA) x - x x d, w yes (Bacon et al., 2000)

OPS-ST National Institute for Public Health and the Environment (RIVM) (The Netherlands) x d, w yes (Van Jaarsveld, 2004)

RIMPUFF Ris0 National Laboratory (Denmark) - x d, w yes (Thykier-Nielsen et al., 1999)

SCREEN3 EPA (USA) x - - - - no (EPA,1995)

a Deposition included (d = dry, w = wet). b Particle Size Distribution (PSD) included.

complex Navier-Stokes equations for fluid dynamics (Kim and Moin, 1985; Stull, 2000). The advection-diffusion equation describes the movement of particles influenced by the wind and turbulent diffusion:

dC _ u dC v dC w dC + d dt dx dy dz dx

d_ d y

Ky d y

Kz d z

all in units of number of pathogens per m3 per unit of time. The factor on the left-hand side describes the local change in concentration (C) in time; Q is the emission rate; the next three factors on the right-hand side describe the transport (advection) by the mean wind speed (u,v,w) in directions [x,y,z]; the final three terms describe the transport by turbulent motions where K is the (eddy) diffusion coefficient [m2 s-1].

Lagrangian models also solve the advection-diffusion equation, but they simulate particle or air transport relative to a frame moving with the mean flow as if an observer moves with a particle (Jacob, 1999). Lagrangian models are able to create backward and forward trajectories to visualise the origin and destination of particles or air.

Eulerian and Lagrangian models are suited for simulation of homogeneous and steady-state conditions, as well as for heterogeneous and non-steady state conditions, and for flat surfaces as well as for terrains with much topography (Holmes and Morawska, 2006). Their general spatial resolution is in the order of several kilometres to thousands of kilometres. A particular form of Eulerian and Lagrangian models are those based on Computational Fluid Dynamics (CFD) that numerically solve the Navier-Stokes equations. CFD-models are very useful in complex terrains, such as mountains or urban environments, where the spatial scales of interest are close to the scales of landscape features. A disadvantage of CFD models is the large amount

J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21

of detailed information (including meteorological) required for simulation.

2.4. The Gaussian dispersion equation

A relatively simple solution of the advection-diffusion equation is the Gaussian dispersion equation (Nickovic et al., 2001; Pasquill, 1974) traditionally used in atmospheric dispersion studies:

C(x, y, z) _

2n • U • ay(x) • az(x)

■ exp

az (x)

z + H az(x)

• exp

where Q is again the emission rate, U is the wind speed [m/s], H is the emission height [m], and ay(x) and az(x) are the diffusion factors in they and z directions [m] (EPA, 1995). The second factor ofEq. (2) describes the crosswind dispersion (in the y-direction). The third and fourth describe vertical dispersion in the z-direction without and with reflections from the surface (by assuming a virtual source at -H m height). The last factor describes inactivation with rate k [s-1 ] (Lighthart and Frisch, 1976; see Section 2.5).

The equation predicts the concentration at any location downwind of a source and assumes a Gaussian distribution of the particles in the crosswind (y) and vertical (z) planes (Dungan, 2010). The plume axes are always projected with respect to the wind direction with transport in the downwind direction [x] solely caused by advec-tion by the wind. Eq. (2) assumes steady-state approximations, i.e. no parameter is time-dependent (Holmes and Morawska, 2006).

A Gaussian plume model includes Eq. (2) in a fixed frame, whereas a Gaussian puff model includes the equation nested in a Lagrangian or trajectory model. Puff models split a continuous plume in discrete particle packets, each individually transported, dispersed and evolving in size (Scire et al., 2000). The model determines the contribution of a puff to the concentration at a receptor.

2.5. Inactivation rate

Inactivation is the process of death or elimination of pathogens due to certain environmental or meteorological conditions (Jones and Harrison, 2004; Pica and Bouvier, 2012; Stärk, 1999), such as high temperatures (generally increasing inactivation), ultraviolet radiation (increasing), and humidity (both decreasing and increasing) (Al-Dagal and Fung, 1990; Jones and Harrison, 2004; Zhao et al., 2014). Dust and droplets may protect pathogens to fluctuations in meteorological conditions, such as dehydration (Mandrioli, 1998; Zhao et al., 2014). In case of re-aerosolisation, survival conditions in soil and water should also be considered (Franz et al., 2014; La Scola and Raoult, 2001; Rzezutka and Cook, 2004; Weber and Stilianakis, 2008).

et al., 2010). Dosimetry models can also include particle size distribution and deposition (Isukapalli et al., 2008). Examples of such models include those of Anjilvel and Asgharian (1995), Georgopoulos et al. (2005), and Rostami (2009). Once the dose is calculated, a probability of infection can be determined.

Threshold doses are, however, equal to a binary step function and are therefore generally considered inadequate for risk assessment (Teunis and Havelaar, 2000). In contrary, dose-response (DR) models describe the probability of infection (or another health outcome) given a specific dose (EPA, 2012) and are a key ingredient for quantitative microbial risk assessments (QMRA) (Dungan, 2014; Teunis and Havelaar, 2000). The binomial, exponential, hyper-geometric, and beta-Poisson models are most used (EPA 2012; Haas, 2002; Teunis and Havelaar, 2000):

- Binomial: Pinf (n, r) = 1 - (1 - r)n (3)

- Exponential: Pinf (D, r) = 1 - exp(-r • D) (4)

- Hyper - geometric : Pinf (D, a, () = 1 - 1F1(a, a + -D)

- Beta - Poisson

Pinf (D, a, ß) _ 1 - (1 + D)

where Pinf is the probability of infection, n is the number of pathogens, r is the probability that ingestion of a single pathogen results in infection (single-hit), 1F1() is the Kummer confluent hyper-geometric function, D is the mean pathogen dose [g m3], and a and ( correspond to the beta distribution parameters for specific ranges (EPA 2012; Teunis and Havelaar, 2000).

The binomial model is a single-hit model for discrete doses describing the infection probability as a function of the complement of the probability of absence of infection. The exponential model, also a single-hit model, describes the probability of infection given ingestion with a Poisson-distributed dose with mean D. The hyper-geometric model is an integrated version of the exponential model for population averaged doses, allowing for variation in the singlehit probability between individual pathogens and/or between hosts (Teunis and Havelaar, 2000). The beta-Poisson model is an approximation of the hyper-geometric model.

DR-models have been developed for several pathogens, including FMDV (exponential and beta-Poisson) (French et al., 2002), B. an-thracis (mainly exponential) (Bartrand et al., 2008; Huang and Haas, 2009; Toth et al., 2013), the avian influenza virus (time-dependent exponential and beta-Poisson) (Kitajima et al., 2011), Legionella pneumophila (exponential and beta-Poisson) (Armstrong and Haas, 2008; Bouwknegt et al., 2013), and C. burnetii (exponential and beta-Poisson) (Tamrakar et al., 2010). With respect to C. burnetii, Brooke et al. (2013) and Jones et al. (2006) described the C. burnetii singlehit probability being 0.44 or 0.9, respectively; the ID50 was estimated to be 1.18 organisms, and the median dose for illness was estimated to be 5.58 bacteria (Brooke et al., 2013). Berendt et al. (1980) showed that the ID50 for L. pneumophila was <129 organisms and the LD50 (median lethal dose) was 1.4 x 105 organisms.

3. Dosimetry and dose-response models

The next step after having calculated exposure levels using an ADM is to calculate the dose individuals are exposed to. A dose can be expressed as the number of pathogens, infectious units (IU), or colony forming units (CFUs) per unit of volume.

The simplest way to retrieve a dose is to assume the dose is equal to the modelled concentration. However, since not all pathogens are inhaled, variables like inhalation rate and exposure duration might better be included for a more detailed dose estimation (Casal et al., 1997; Dungan, 2014; Li et al., 2013; Low et al., 2007; Schley et al., 2009; S0renson et al., 2000, 2001, Ssematimba et al., 2012; Stellacci

4. Pathogenic bioaerosol studies using atmospheric dispersion models

This section discusses studies using ADMs to simulate dispersion of pathogenic bioaerosols. Our literature search query (see Appendix B) included keywords regarding ADMs, general pathogen keywords (e.g., 'bioaerosols' and 'pathogens'), and pathogens and diseases from a list of the European Centre for Disease Control (ECDC, 2010), from several bioaerosol reviews (Al-Dagal and Fung, 1990; Després et al., 2012; Dungan, 2010; Gilbert and Duchaine, 2009; Griffin, 2007; Griffiths and DeCosemo, 1994; Jones and Harrison, 2004; Monn and Koren, 1999) and from a report on emerging zoonoses in

6 J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21

the Netherlands (Van der Giessen et al., 2010). All non-airborne microorganisms and diseases related to non-airborne microorganisms from the ECDC list have been filtered out as a result of the combination of keywords regarding the ADMs.

In this chapter we reviewed the foot-and-mouth disease virus (Section 4.1), B. anthracis (Section 4.2), the avian influenza A virus (Section 4.3), L. pneumophila (Section 4.4), C. burnetii (Section 4.5), and the Pseudorabies virus (Section 4.6). Section 4.7 comprises other pathogens.

4.1. Foot-and-mouth disease virus

The foot-and-mouth disease virus (FMDV) affects cloven-hoofed animals, mainly cattle, sheep, and pigs, and is highly infectious (Kitching et al., 2005). Major transmission routes include airborne spread from infected farms, movement of infected livestock or contaminated persons, objects and animal products, and excretion of urine, faeces, semen and tissues (Alexandersen et al., 2003; Cottam et al., 2008; Donaldson, 1997; Kitching et al., 2005; Klein, 2009; Pharo, 2002). The incubation period varies from four to fourteen days (Sellers and Forman, 1973). Large outbreaks occurred in countries including Canada, the United Kingdom, France, Germany, Denmark, Spain, the Netherlands, and South Korea (Alexandersen et al., 2003; Bouma et al., 2003; Carrillo et al., 1990; Cottam et al., 2008; Donaldson et al., 1982; Kritana et al., 2014, Sellers and Daggupaty, 1990; Sellers and Forman, 1973; Sorenson et al., 2000; Valarcher et al., 2009). We identified four main analysis techniques for ADM simulation. Several studies simply reconstructed the geographical virus spread. Some subsequently applied a threshold dose to identify the farms at risk. Thirdly, a few applied a dose-response function to calculate a probability of infection or a sensitivity and specificity rate.

4.1.1. United Kingdom, 1966-1968

In 1966-1968, four districts in the UK were affected by FMDV (Gloster et al., 2005b; Henderson, 1969; Sellers and Forman, 1973; Smith and Hugh-Jones, 1969). Daily dosages at surrounding farms were calculated with a simplified version of the Gaussian dispersion equation (Blackall and Gloster, 1981; Gloster et al., 1981). A threshold dose of 1 infectious unit (IU) (basically 1 virus particle) was applied to conclude there was a "very close agreement" between predictions and observations. Note that currently more advanced dose-response models are available (Section 3) that supersede the infectious dose paradigm.

The epizootic was reconstructed later in several other studies of varying quality. Although developed for gas dispersion modelling, (Casal et al., 1997,1995) used the Areal Locations of Hazardous Atmospheres (ALOHA) model and applied a threshold dose to predict the farms' infectivity status. They simply concluded that the predictions "agreed relatively well to the observations" and that the predicted doses per receptor farm were uncertain due to uncertain emission rates and the possible existence of other transmission routes. In other words, predicted concentrations were compared to the observations at farms in a qualitative way, and estimated infection risks using dose-response models were not calculated to support the hypothesis. (Gloster et al., 2005b) used the Atmospheric Dispersion Modelling System (ADMS) and concluded that all farms could have been exposed (given the 'ideal' meteorological conditions). They discussed that other actual atmospheric stability conditions could explain mis-classified farms.

Schley et al. (2009) used the Numerical Atmospheric-dispersion Modelling Environment (NAME) and did, however, go further by applying an exponential dose-response model. As one of the few, they assessed the quality of their predicted infection risks by calculating a specificity and sensitivity rate, being 82% and 94% respectively regarding the farms' infectivity status. Sanson et al. (2011) showed that

the occurrence of airborne spread was very significant (p ^ 0.00), although other transmission routes could not be excluded. Topographic effects highly influenced the bioaerosol spread and were thus very important to include in such risk assessment studies.

Gloster et al. (2010) performed a comparison study among six ADMs, namely the Californian Puff Model (CALPUFF), the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), the Lagrangian Operational Dispersion Integrator (LODI), the Modèle La-grangien Courte Distance (MLCD), NAME, and the Ris0 Mesoscale PUFF model (RIMPUFF). Unfortunately, the results were not analysed statistically: they simply concluded that all six models could be used for dispersion assessment during outbreaks, although (small) differences in predicted areas at risk were observed.

4.1.2. France and the United Kingdom, 1981-1982

In 1981-1982 an epizootic occurred in Bretagne (France) with successive outbreaks in the UK at the Island of Jersey (75 km north) and the Isle of Wight (250 km north). Donaldson et al. (1982) applied a Gaussian model and concluded that criteria for a successful long-range transmission - favourable wind speed, wind direction, stable atmosphere, and high emission rates - were fulfilled. However, if there was indeed an airborne link between the farms in France and the UK, then more outbreaks in France might have been expected, yet were not reported. Moutou and Durand (1994) used the ICAIR model and explained 10 out of 13 secondary outbreaks, but both their method description and analyses were described very lim-itedly. S0renson et al. (2000, 2001) used RIMPUFF, developed time-dependent species-specific emission rates, and applied a virus inac-tivation rate and an infection probability function. They concluded that transmission to the UK was unlikely, as the predicted virus concentrations were about 500 times lower than the assumed threshold concentration to infect cattle. In a sensitivity analysis they showed that 1000 infected pigs were required to infect susceptible cattle up to 300 km downwind.

4.1.3. United Kingdom, 2001

In 2001 outbreaks occurred at 1849 farms across the UK (Gibbens et al., 2001). Several studies emphasised the important effect of topography, atmospheric stability, and low wind speeds on pathogen dispersion. Gloster et al. (2003) and Mikkelsen et al. (2003) used four models - the Gaussian dispersion equation, NAME, DERMA (Danish Emergency Response Model of the Atmosphere), and RIMPUFF. They determined specific emission periods and the areas at risk, and simulated dispersion, taking into account local topography effects. They explained seven out of 12 infected farms. Gloster et al. (2005a) also used multiple models - NAME, ADMS and DERMA - for comparison during very stable atmospheric conditions. They could explain airborne infections in two out of three epizootic clusters. However, in none of these studies relevant quantitative analyses were performed; a dose-response model was not used nor was the infection risk characterised.

Exposure from another potential source of FMDV, burning carcases, was investigated in a series of studies using NAME (Champion et al., 2002; Gloster et al., 2001; Jones et al., 2004). In one of them (Jones et al., 2004) an exponential dose-response model was used to estimate the probability of infections in cows and sheep downwind, being less than 0.3% and 0.04%, respectively. They concluded that infection from burning carcases was therefore unlikely. However, taking into account the presence of hundreds or thousands of livestock animals at several kilometres downwind of a plume, a few infections might have been possible (indeed, one farm was infected).

4.1.4. Other epizootics

S0renson et al. (2000 , 2001 ) simulated dispersion from Germany to Denmark in 1982 using RIMPUFF and assumed emission from 1000

J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21 7

pigs. These emission rates combined with stable atmospheric conditions and favourable wind conditions gave high infection risks in Denmark. However, when fewer pigs were assumed or cattle or sheep were considered as a source, then the probability of infection decreased tremendously. Since the number of infected animals was chosen arbitrary, it is, however, arguable whether the conclusion will hold if the actual number of infected animals would have been lower, or cattle or sheep were shedding instead of pigs.

Traulsen et al. (2010) also used RIMPUFF and simulated another (unspecified) epizootic in Germany comprising 729 farms. They performed a risk factor analysis using Monte-Carlo simulations including data on emissions, farm locations, and livestock susceptibility. Significant correlations were found between modelled concentration, several time parameters, farm type, farm density, and control strategies. Although these results are plausible, technical details were lacking unfortunately.

In a subsequent study they investigated whether simple fuzzy logic could replace complex ADMs (Traulsen and Krieter, 2012). That is, they replaced numerical data (spatial coordinates, wind speed, atmospheric stability, and modelled concentrations) by factors (low/medium/high). Compared to a Gaussian dispersion model, the sensitivity and specificity rates were on average 81% and 97%, respectively. However, the authors assumed true predictions from the Gaussian model, technical details were lacking, meteorological data were still required for their analysis, and the converted modelled concentrations were simulated by an ADM.

Maragon et al. (1994) and Moutou and Durand (1994) used the ICAIRpuff model to simulate FMDV spread in northern Italy in 1993 in a rather qualitative way. The areas at risk were small due to low wind speeds, a low humidity, and limited farm contact, but airborne transmission to nearby farms could potentially have occurred according to the model. Additional hypothetical emission from two pig farms resulted in "many more farms at risk". The model was proposed as a tool for decision-makers for future outbreaks, although the analyses were highly qualitative and no infection risks were calculated.

Schley et al. 2009 simulated an epizootic in the UK in 2007 using NAME. They marked four farms as primary sources and predicted the infection risks for all other farms. The sensitivity rate was about 67% (4 out of 6 farms); the specificity rate was approximately 92%.

Daggupaty and Sellers (1990) explained airborne infection at all twelve infected farms in Canada (1951-1952) using a Gaussian plume model, although additional infection routes (such as movement of persons and livestock) could not be excluded at six of the farms.

Finally, two epizootics in South Korea (2010-2011) were simulated with CALPUFF, of which one was explained by airborne transmission (Kritana et al., 2014). Transport of faeces and contaminated persons potentially increased infection probabilities. The authors used threshold values for inactivation by humidity to determine the most likely period of transmission. In addition, a smartphone application was highlighted for FMDV dispersion simulations, requiring data on serotypes, inactivation rates, and farms (location, livestock type, and livestock numbers) to simulate dispersion during field measurements.

4.1.5. Hypothetical simulations

Two series of studies were published discussing the possible consequences of FMDV outbreak in Australia and Austria. In Australia, the number of infectious units and virus deposited per hectare as a function of distance from a random initial source was simulated using the Gaussian plume equation (Cannon and Garner, 1999; Garner and Cannon, 1995). It was, simply, concluded that Australian weather conditions would not be a limiting factor for virus spread.

A more advanced risk assessment module was subsequently developed containing HYSPLIT, an intra-virus production model including five infectivity statuses (susceptible, latent, infectious, immune and death), and a binomial dose-response model (Garner et al., 2006;

Hess et al., 2008). The number of farms at risk was calculated from a dose-response model. An additional sensitivity analysis showed that virus strain, pathogen inactivation and temperature would largely affect concentration; relative humidity was of minor importance (Hess et al., 2008).

In Austria, a Gaussian plume decision-support system for potential outbreaks was developed (Rubel and Fuchs, 2005), with a special focus on atmospheric stability, emission rates and wind speeds, where atmospheric stability was of great importance (concentrations at two kilometres from a potential source varied by a factor of 1,000 between unstable and highly stable conditions). The effect of wind speed was much smaller. Mayer et al. (2008) subsequently proposed an improved (Lagrangian) model for complex dispersion in the mountainous Austrian landscape. Case studies depicted a significant influence of local wind systems on the airborne spread. The authors concluded that the model was an appropriate tool for risk assessment of airborne virus spread. Although their ADM was more advanced than most other models described in this section, they only made a limited assessment of the risk by applying threshold doses.

4.1.6. Summary

• Quantitative analyses were only performed in a minority of studies. Most studies did not go further than geographical visualisation or determination of the number of farms possibly affected (using a threshold concentration or dose for infection). Only a few studies used a dose-response function and determined the sensitivity and specificity rate regarding the farms' infectivity statuses.

• Atmospheric stability and landscape topography were depicted as important factors influencing the surface concentrations and thus the exposure levels.

• Most studies focused on short-range transmission up to several kilometres. Long-range transmission could not be proven, although ADMs are suited for it.

4.2. B. anthracis

B. anthracis is a spore-forming bacterium causing anthrax in humans and animals through exposure to infected livestock or contaminated animal products (Pohanka and Skladal, 2009). Its spores are very resistant to extreme physical conditions, such as desiccation, heat, and disinfection (Shafazand et al., 1999). The incubation period is only two to six days (Pohanka and Skladal, 2009).

B. anthracis is a highly pathogenic bioterrorism-related agent (Anderson and Bokor, 2012). Outbreaks or releases have rarely been described, but some intentional releases caused much concern (Shafazand et al., 1999). In 1993, spores were aerosolised by a Japanese cult, although no one was infected (Keim et al., 2001). In 2001, a total of 22 cases were identified in the United States who were exposed to contaminated mail (Jernigan et al., 2002).

The number of publications regarding atmospheric dispersion modelling of B. anthracis is limited. Meselson et al. (1994) simulated the release from a military facility in the former Soviet Union in 1979 that had resulted in 77 human infections (of whom 66 were lethal), and death of livestock up to 50 km downwind. They estimated that approximately four billion spores had been released.

Due to its high pathogenicity and biosafety classification, multiple emergency preparedness models have been developed. Stuart and Wilkening (2005) simulated the dispersion of 1015 spores in an urban environment with the Gaussian dispersion equation and a dose-response model. The maximum distance of lethal infections varied from 25 km up to more than 200 km, dependent on the chosen decay function. The authors highlighted the need for predictive models to efficiently provide information during a crisis. A very similar analysis was performed in two other studies (Craft et al., 2005; Wein et al., 2003). Their emergency response tool also contained sub-models on

8 J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21

(age-dependent) dose-response, disease progression, antibiotic distribution and hospital care. They calculated an average probability of infection of about 65% at 200 km downwind. Buckeridge et al. (2006) developed a tool including a Gaussian plume model, a dose-response model, disease states, clinical visits, and pharmaceutical prescriptions. They estimated that the number of infected persons would range from 15,000 (0.01 kg anthrax) to 49,000 (1 kg). Nicogossian et al. (2011) used the Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) model to simulate a hypothetical release of one million spores in the subway of Washington D.C. (USA), with subsequent dispersion in the outdoor environment. They concluded that a significant number of commuters and resident would have been exposed and being overload the existing health care infrastructure. Isukapalli et al. (2008) used CALPUFF for a hypothetical release of 1012 spores in an urban environment in New Jersey (USA) and accounted for activity patterns and physiological variability. Uncertainty analyses with respect to atmospheric conditions, population demographics, emission rate, and other characteristics were expressly recommended for future analyses. They also advised to include a source characterisation option in a comprehensive planning scheme for detecting bioterrorism-related releases. Finally, Tang et al. (2009) performed a meteorological flow field analysis to locate a source of a hypothetical anthrax release in an unsteady three-dimensional atmospheric wind field in an urban street canyon "with high accuracy".

Except for the latter publications, all these publications with emergency preparedness models have not only included a realistic quantified emission rate, but they also have performed a full quantitative microbial risk assessment, including the steps of (1) hazard identification, (2) dose-response relationships, (3) exposure assessment, and (4) risk characterisation.

4.2.1. Summary

- Only one retrospective simulation modelling study regarding the dispersion of B. anthracis was published.

- In several other studies a full emergency response model was developed taking all QMRA steps into account, including a quantified emission rate, an ADM and dose-response model, and clinical consequences.

4.3. Avian influenza virus

Influenza viruses are widespread and due to their high mutation rate many subtypes exist. Poultry farms are an important reservoir for the avian influenza virus (AIV) (Peiris et al., 2007), which play a critical role in the genesis of pandemic influenza viruses (Shortridge, 1992). AIV transmission to humans is largely facilitated by contact with animals and excretion of contaminated droplets or aerosols (Killingley and Nguyen-Van-Tam, 2013), and to a lesser extent through transport of (dead) birds or contaminated objects (vehicles, humans, or fomites), water, food, and contact with infected wildfowl or insects (Dent et al., 2008). Major outbreaks of avian influenza have occurred in China, Italy, the Netherlands, and Thailand (Areechokchai et al., 2006; Capua et al., 1999; Chan, 2002; Ellis et al., 2004; Koopmans et al., 2004; Stegeman et al., 2004; Tang and Chen, 2013).

The number of studies with AIV dispersion simulations is limited. Ssematimba et al. (2012) developed a Gaussian plume model and simulated the epizootic in the Netherlands in 2003. The authors highlighted the need for quantification of dispersion patterns to understand pathogen transmission between farms. By means of an exponential dose-response model they estimated that the airborne route accounted for 24% of new infections within 25 km of a source farm. That is, airborne AIV dispersion could have played a significant role in short-range transmission, but it could not completely explain

long-range transmission. If, however, the epizootic had been reconstructed in time, then potentially the transmission from sources to susceptible farms, that subsequently would act as new source farms, might have been identified, thus potentially increasing the percentage. The results discussed in the sensitivity analyses were in accordance with the theory: wind speed (negative), deposition velocity (positive), emission height (negative) and inactivation (negative) influenced the probability of infection. These effects were largest at distances up to 2 km.

Seo and Lee (2013) and Seo et al. (2014) applied CFD modelling (Fluent) to the South Korean outbreak of 2008. The possibility of spread from each of the 39 farms to another was calculated as a function of wind direction and three different wind speeds. However, a thorough analysis on the results was not presented, although two other transmission networks were added in an additional study, namely a medicine and feed network, revealing that all contributed (Leeet al., 2014).

4.3.1. Summary

- The number of AIV studies in which an ADM was applied is limited.

- In one study the airborne route accounted for 24% of total transmission for distances up to 25 km.

4.4. L. pneumophila

Legionnaire's disease (or legionellosis) in humans is caused by a respiratory infection with the bacterium L. pneumophila (Leclerc et al., 2002). Inhalation of bacteria originating from natural fresh-water, potable water, cooling-towers or soil is the most likely cause of infection (Bovallius and Roffey, 1987). Large outbreaks have been associated to cooling-towers such as reported from Spain (6x), Australia, UK (3 x), Italy, France, Sweden, US, New Zealand, Norway, Canada, the Netherlands, and Germany (Walser et al., 2014).

Legionella dispersion from cooling-towers was simulated in several studies. Nguyen et al. (2006) simulated an outbreak in France (2003-2004) with 86 human cases including 18 lethal infections using the ADMS model. The analysis was rather visual and qualitative: it was concluded that the model showed "good coverage ofthe municipalities where cases lived". We re-analysed the predicted concentrations and attack rates per municipality with a linear regression function in R (version 3.1.2) showing that the correlation was indeed positive, but not significant (p ^ 0.12).

A Norwegian outbreak (2005) with 56 human cases including ten lethal infections was modelled in a series of four studies. Nygard et al. (2008) performed a similar analysis to that of Nguyen et al. (2006) and used the Integrated PUFF model (INPUFF). An air scrubber at a biological treatment plant as source gave the best fit and resulted in the highest number of cases exposed. In three subsequent studies CFD modelling was applied to investigate whether Legionellae were indeed generated from that specific source (Blatny et al., 2011, 2008; Fossum et al., 2012). Modelling results were used to select optimal sampling sites in the area, thereby detecting bacteria up to 200 metres downwind (Blatny etal., 2008). Additional measurements on particle size distribution revealed that the majority of the bacteria were captured in either small (<4 ^m) or large (>16 ^m) size fractions (Blatny et al., 2011).

4.4.1. Summary

- A limited number of studies described dispersion modelling of L. pneumophila, all regarding industrial water units as source.

- ADMs were used in epidemiological studies to attribute the source of local epidemics.

J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21 9

4.5. C. burnetii

Q fever is a zoonotic disease caused by the bacterium C. burnetii (Parker et al., 2006). It is present worldwide with the exception of New Zealand. Ruminants are its main host (Angelakis and Raoult, 2010), which excrete the pathogen via their birth products, milk, faeces, and/or urine (Arricau Bouvery et al., 2003; Berri et al., 2002; Guatteo et al., 2007,2006; Kim et al., 2005; Rodolakis et al., 2007; Van den Brom et al., 2012). Human infections occur from inhalation, leading to asymptomatic infections, (mild) clinical signs (e.g., fever and headache), more severe disease (pneumonia or hepatitis), or even mortality (Angelakis and Raoult, 2010; Dijkstra et al., 2012; Parker et al., 2006). Large outbreaks have occurred in countries including Canada, France, Germany, Italy, Slovakia, Switzerland, the Netherlands, the United Kingdom, and the United States (Dupuis et al., 1987; Georgiev et al., 2013; Hawker etal., 1998; Kovacova et al., 1998).

Despite its global abundance, the number of ADM studies regarding C. burnetii transmission is limited. An outbreak in the United Kingdom (2007) with 30 human cases was simulated to a limited extent with NAME to identify its source (Wallensten et al., 2010). Results showed that airborne transmission from all selected potential sources could have occurred. Extra difficulties arose due to a lack of emission data and timing of infection.

The outbreak in the Netherlands (2007-2010) was the largest Q fever epidemic ever described with over 4000 notified human cases. Infection was mainly associated with large dairy goat farms (Roest et al., 2011). Three regional epidemics were simulated with the Operational Priority Substance Short Term (OPS-ST) model (Sauter et al., 2011; Van Leuken et al., 2015). Due to the absence of quantified emission rate data, three simple emission profiles were defined and the best linear fit of the incidence-concentration function compared to a model with no predictors and one with distance as a single predictor were determined. In all three areas the ADM-concentrations correlated significantly better to the observed incidence than those of two simple models. Better emission rate parameterisations would improve the simulation modelling, thus allowing for a quantified risk assessment.

4.5.1. Summary

- The number of ADM studies simulating C. burnetii dispersion is very limited, despite the large number of epidemiological studies on Q fever.

- (Better) emission rate data would improve risk assessment.

4.6. Pseudorabies virus

Aujeszky's disease is caused by the Pseudorabies virus (PRV) (Mettenleiter, 2000) with domestic pigs and wild boar as principal hosts (Christensen et al., 1993; Ruiz-Fons et al., 2008). Outbreaks have occurred in countries including Denmark, Germany, Ireland, Poland, the United Kingdom, and the United States (Christensen et al., 1993; Gloster et al., 1984; Henderson et al., 1995; Müller et al., 2003; Obaldia, 2005; Salwa, 2004; Scheidt et al., 1991).

No recent studies on PRV dispersion modelling were found in the literature. Gloster et al. (1984) simulated PRV dispersion among eleven pig herds in the UK (1981-1982) using the Gaussian dispersion equation. They suggested that airborne transmission was possible in seven out of 11 herds. Transmission via other routes was classified as unlikely at the majority of the farms. As with the FDMV dispersion studies, a quantitative analysis (including dose-response models) was not applied.

Casal et al. (1997) re-analysed this epizootic and another in the United States (1988) among 10 pig herds using ALOHA and assumed constant wind speeds. The predicted number of infected animals was strongly dependent on the chosen wind speed. It was noted that in reality virus concentrations would not be homogeneous and

the amount of virus inhaled is Poisson-distributed. Thus, despite a low mean dose, individual animals might have been infected. Furthermore, Grant et al. (1994) also analysed the U.S. outbreak with the Gaussian dispersion equation. During the outbreak, stable atmospheric conditions, strong winds and low temperatures occurred, which are favourable conditions for spread of airborne pathogens.

4.6.1. Summary

As with the FDMV dispersion studies, quantitative analyses were lacking. Although airborne spread was explained at most farms, the level of geographical visualisation or determination of the number of farms possibly affected was not exceeded.

4.7. Other pathogenic bioaerosols

4.7.1. Livestock and urban environments

Gloster (1983) analysed two outbreaks of Newcastle disease among poultry farms in the UK in 1959-1960 and 1969 with a Gaussian plume model (although, comparable to FMDV and PRV, rather limitedly). They classified the estimated daily virus doses as "very low". Infections up to 8 km from the initial source were explained.

Models based on Computational fluid dynamics (CFD) are suited to simulate dispersion in urban environments, which differ from rural environments due to large differences in turbulent conditions (Gao et al., 2008). The Severe Acute Respiratory Syndrome (SARS) virus and the human influenza virus are typical urban viruses with humanhuman transmission. Yu et al. (2004) used Fluent to simulate a SARS virus outbreak in Hong Kong in 2003 with 187 cases. Virus was excreted from a bathroom and transported outdoor by an exhaust fan. Modelled exposure correlated significantly to the expected exposure (homes of cases) in six nearby buildings. Liu and You (2012) simulated human influenza virus transmission among five apartment buildings. Virus particles were dispersed tens of metres downwind, thereby leading to a "high risk of secondary infection in large areas". However, technical details and extended analyses were lacking.

4.7.2. Wastewater

In several studies measured concentrations close to wastewa-ter treatment plants (WWTP) were compared to predicted ADM concentrations. In some studies ADMs were even used to quantify the contamination of the surrounding environment. For instance, Stellacci et al. (2010) performed a QMRA and simulated the dispersion of Cryptosporidium, Campylobacter, and rotavirus in the surrounding of an Italian WWTP. Concentrations at 100 m downwind were lower than the limits for drinking water. Dungan (2014) performed a QMRA on exposure to Campylobacter jejuni, Escherichia coli, Listeria monocytogenes, and Salmonella spp. from irrigation of diluted dairy wastewater. They used the AMS/EPA Regulatory Model (AER-MOD) and found maximum relative exposure risks of-5.8 and -0.1 (10log) at 1000 m downwind (both for C. jejuni), assuming inacti-vation rates of 0.07 s-1 (day) and 0.002 s-1 (night), respectively. It was recommended scheduling irrigation events to day-time, given the higher wind speeds causing more dilution, and higher inactiva-tion rates due to desiccation and ultraviolet light exposure.

Furthermore, Sorber et al. (1976) detected coliform bacteria, including Klebsiellae and faecal Streptococci in air samples near a WWTP in Arizona (USA) and used a Gaussian plume model for concentration predictions. By means of a linear regression model (R) we re-analysed their data showing that a high correlation (r = 0.72) existed between the measured and modelled data, although borderline statistically significant (p = 0.07).

Teltsch et al. (1980) detected E. coli in air samples near an Is-raelian WWTP and used the Gaussian dispersion equation for prediction modelling. A highly significant correlation was found (r = 0.93, p ^ 0.00). Holden and Babcock (1985) measured total viable particle concentrations near a WWTP to retrieve an emission rate using

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the Gaussian dispersion equation for back-calculation. However, they found a positive inactivation rate, indicating the contribution of other sources. Li et al. (2013) measured mesophilic bacteria concentrations at several points downwind of a WWTP rotating-brush aerator and determined emission rates. They used a Gaussian plume model (Dowd et al., 2000) and created source depletion curves, resulting in "acceptable low risk values at various downwind distances". Peterson and Lighthart (1977) analysed exposure to municipal wastewater from cooling towers. They discussed the effects of particle size, source height, wind speed, inactivation, and atmospheric stability on airborne concentrations.

4.7.3. Biosolids

Aerosolisation of pathogens from biosolid material, such as domestic sewage sludge or compost, may also occur. Rotavirus, coron-avirus, Salmonella spp., and E. coli were detected at various distances from a field at which sewage sludge was applied as fertiliser (Dowd et al., 2000). Concentrations were predicted with the Gaussian dispersion equation and converted to infection risks as a function of exposure duration and various wind speeds, yielding maximum risks of 37.3% (bacteria) and 100% (viruses) (given 24-h exposure at 100 m distance) (unfortunately, confidence intervals were not given).

Furthermore, emission rates of mesophilic actinomycetes and the fungus Aspergillus fumigatus from static compost windrows were determined in a series of studies (Taha et al., 2005, 2006, 2007). A wind tunnel was used to determine emission rates; the models SCREEN3 and AMDS were used to generate concentration profiles as a function of distance. The concentrations reduced to background values of 1000 CFU/m3 within 100-250 m of the source site, corresponding to legal requirements. Low et al. (2007) compared Clostridia, Chlo-roflexi, and Euryarchaeota concentrations downwind of a land application site in Arizona (USA) with concentrations predicted with the Gaussian dispersion equation. The Gaussian model was classified as adequate for predicting concentrations downwind from the site.

4.7.4. Other

Finally, several ADM studies had no specific sources included. Lighthart and Mohr (1987) investigated reovirus (Respiratory Enteric Orphan virus) concentrations with a Gaussian plume model and concluded that wind speeds largely influenced the source depletion curves, thereby being 'potentially important' as a dilutor. Lin et al. (2014) measured and predicted (using a Gaussian plume model) Fusarium concentrations. Concentrations were generally lowest in winter. Sprigg et al. (2014) simulated dispersion of the fungi Coccid-ioides immites and Coccidioides posadasii, the causative agents of human valley fever. They used the Dust Regional Atmospheric Model (DREAM) and could associate the occurrence of a large dust storm and specific vegetation and land cover data to a Californian epidemic in 2011 with 3600 cases.

The abundance and diversity of airborne pathogenic and non-pathogenic bacteria and fungi at 2700 m altitude were measured in the United States (Smith et al., 2012, 2013). They created back-trajectories using HYSPLIT and concluded that the microorganisms originated from China or Japan. Thus, after 10 days travelling across the Pacific Ocean, viable microorganisms were still detected.

4.7.5. Summary

- Dispersion in urban environments was usually modelled with CFD techniques. Dispersion at field sites was usually modelled with Gaussian models.

- In addition to sources related to livestock and urban environments, exposure from WWTPs and biosolids was modelled. A few studies focused on exposure from WWTPs also performed a QMRA.

- In several studies actual infection risks were calculated with dose-response models.

5. Model parameterisations

51. Emission

Three pragmatic approaches were identified from the studies discussed in Section 4, namely the use of:

- Arbitrary emission data, thus leading to relative concentration maps (Fossum et al., 2012; Gloster et al., 1984; Lighthart and Frisch, 1976; Sorber et al., 1976; Van Leuken et al., 2015; Wallen-sten etal., 2010).

- Realistic assumptions, such as a release of 1015 B. anthracis spores (Craft et al., 2005; Isukapalli et al., 2008; Nicogossian et al., 2011; Stuart and Wilkening, 2005), or the use of morbidity, severity and duration of the disease as a proxy for emission (Gloster, 1983) -although the reliability may be arguable.

- Varying emission rates through sensitivity analyses (e.g., Buckeridge et al., 2006)

In all other studies measurements (Section 5.1.1) or emission models (Section 5.1.2) were applied to determine emission rates.

511. Measurements

The simplest method to retrieve emission rates from measurements is by determining a concentration and flow rate:

Q = C ■ U ■ A

where Qis the emission rate [pathogen amount per second], C is concentration [pathogen amount per m3 ], U is wind speed [m/s], and A is the cross-sectional area from which the pathogen is released [m2 ]. An alternative method to determine the flow rate (U^A) is by using the eddy covariance technique, that is used to measure vertical turbulent fluxes (e.g., Kormann et al., 2001 )

Holden and Babcock (1985), who focused on pathogen release from a WWTP, found a rate of approximately 20,000 aerosols per second using Eq. (3). Similar assessments were performed by Dowd et al. (2000) and Taha et al.(2006).

Taha et al. (2005) determined a compost windrow emission rate of A. fumigatus through a wind tunnel experiment using:

Q = Ф

where 0 is the flow rate [m3 s-1]. Note that in fact, Eqs. (3) and (4) are similar, except for that Eq. (4) describes the emission per surface unit instead from a point source.

Paez-Rubio et al. (2007) incorporated multiple measurements of total bacteria, total coliforms, Clostridia, and endotoxins in a vertical column (Holmen et al., 2001):

U(z) ■ C(z) ■ t ■ cos (в) W

which is in fact an extended version of Eq. (4). Eq. (5) is an integration in height from roughness length z0 [m] to the top of the plume h [m], taking into account height-dependent wind speed and concentration data. The emission rate was scaled by time t [s], the angle between wind direction and field edge (0), and the width of the column W [m].

Finally, emission rates were derived from measurements at one or several distances from a source that were incorporated into a dispersion model. Li et al. (2013) measured concentrations of mesophilic bacteria at two, five and ten metres downwind of a WWTP and thus determined Q. Low et al. (2007) calibrated the Gaussian dispersion equation by comparing modelled and measured concentrations at five metres from a bio-solid source.

There was no livestock-related ADM study in which measurements were incorporated.

J.P.G. Van Leuken et al./Microbial Risk Analysis ООО (2015) 1-21

[m5G;August 3, 2015;13:27] 11

5.1.2. Pathogen production models

Pathogen production models are useful in case of livestock-borne pathogens. For FDMV an intra-farm virus production model was developed for cattle, sheep and pigs, giving the virus amount emitted per infected premise per day (S0renson et al., 2000, 2001). It was used in several dispersion studies (Gloster et al., 2005b; Mikkelsen et al., 2003; Traulsen et al., 2010). A major disadvantage, however, is the lack of dynamics within a population, which herd dynamics models include. Such models are based on multiple infectivity states, such as 'susceptible', 'latent' (infected but not yet infectious), 'infectious', 'recovered', or 'dead'. Herd dynamics models calculate the number of individuals in and the transfer rate between each compartment per time unit, where the reproduction ratio R0 is defined as the average number of secondary infections caused by one infectious individual introduced in a naive population. The infection will fade out if R0 < 1 (either naturally or enforced, e.g., by vaccination) (Bouma, 2005; Hogerwerf et al., 2011); ifR0 = 1, the infection will be sustained and if R0 > 1 an epidemic will occur.

Garner et al. (2006) and Hess et al. (2008) used a Rinderpest population model (James and Rossiter, 1989), which applies Monte-Carlo simulations and gives the number of susceptible, (partially) immune, affected, and mildly affected cattle and wildlife, and allows for vaccination intervention. Specific FMDV data regarding the length of the latent, infectious, symptomatic, and excretion period, mortality rate, and maximum daily virus production were retrieved from literature (Alexandersen et al., 2003; S0renson et al., 2000).

Examples of other population dynamic models (although not used for ADM simulations) are those for C. burnetii, the SARS virus, and the Porcine Reproductive and Respiratory Syndrome virus (Courcoul etal., 2011; Hogerwerf et al., 2013; Naheed et al., 2014; Nodelijket al., 2000).

5.2. Inactivation

In the ADM studies, either threshold values related to environmental conditions (Section 5.2.1) were used, or inactivation rates as a function of time were applied (Section 5.2.2). Therefore, to improve risk assessment, much more effect should be invested to determine pathogen specific inactivation parameterisations as a function of environmental conditions.

5.2.1. Threshold values

Many studies, primarily those that focused on FMDV, applied threshold inactivation values. Historical measurements showed that FMDV survival was highest for a relative humidity > 60%, reduced for 20-60%, and very small for < 20% (Donaldson, 1972). There is also a (qualitative) description on the effect of temperature: FDMV would survive for "long periods" at "low" temperatures and for "considerable periods" at temperatures in the range of 20-27 °C (Donaldson, 1972; Donaldson and Ferris, 1975; Gloster et al., 2005a).

These values were used in several studies. Kritana et al. (2014) used the threshold value for relative humidity to determine the most likely period of transmission in a sequence of days. Gloster et al. (2005a) assessed the FMDV viability in a qualitative way given the observed temperature; and Cannon and Garner (1999) assessed the probability of a major FMDV outbreak given the climatic conditions in Australia. That is, they concluded that weather conditions would not be a limiting factor for airborne FMDV spread (unfortunately, technical details are lacking).

In several other FMDV studies modelled concentrations were set to zero in case of a relative humidity < 60% (Casal et al., 1995; Gloster et al., 1981). A similar approach was applied to PRV concentrations (Grant et al., 1994): 100% survival in case of a relative humidity >85%, no survival for <25% and a linear survival curve for 25-85%.

5.2.2. Rates

In many other studies inactivation was expressed as a rate, i.e. as a decrease in time (cf. Eq. (2)). Rates for rotavirus (2.86 x 10-2 s-1), coronavirus (2.66 x 10-2 s-1), Salmonella spp. (2.35 x 10-4 s-1), and E. coli (1.92 x 10-4 s-1) were used in a wastewater simulation study (Dowd et al., 2000). Sorenson et al. (2000) proposed a rate for FMDV inactivation (3.2 x 10-4 h-1, or 8.9 x 10-8 s-1), which, however, might be very small compared to historical measurements (Donaldson, 1972). Nevertheless, it was adopted by several other studies (Garner et al., 2006; Hess et al., 2008; Traulsen et al., 2010).

Ssematimba et al. 2012 assumed an AIV inactivation rate of 2.89 x 10-6 s-1. Additional sensitivity analyses (with rates from 4.0 x 10-7 to 2.0 x 10-6 s-1) showed that the effect on infection risk was about 10-20%. Lighthart and Frisch, (1976) and Peterson and Lighthart (1977) incorporated higher decay rates of 0, 0.001, 0.01, and 0.1 [s-1 ] in their Gaussian model description. They showed that, for the two highest decay rates, the majority of the viable cells were inactivated within 1-10 km from the source. Li et al. (2013) assumed rates of 0.0, 4.0 x 10-3, 6.0 x 10-3, 0.02, and 0.12 s-1 and created source depletion curves in their WWTP investigation. The highest in-activation rate resulted in a four times lower concentration compared to the lowest rates.

For B. anthracis four exponential decay models were proposed (Stuart and Wilkening, 2005), despite B. anthracis being very persistent. These models were defined as a function of time with an inac-tivation rate of 1.67 x 10-4 s-1 (although the actual inactivation rate is very much dependent on the amount of UV radiation and ozone concentration (Spotts Whitney et al., 2003)).

6. Probability of infection

The simplest technique to assess a risk (comparing doses to threshold values) was applied in many FDMV studies, e.g., 0.06 (cattle), 1.11 (pigs), and 7.70 (sheep) TCID50/m3 (Gloster et al., 2005a, 2005b, 2010, 2003, , 2001; Mikkelsen et al., 2003; Rubel and Fuchs, 2005; S0renson et al., 2000; Traulsen and Krieter, 2012). TCID50 is the median dose to infect a tissue culture; however, the actual meaning of this measure can be disputed, since it represents a probability of infection and not a concentration or dose. Furthermore, threshold values were also used in several other studies (Blatny et al., 2011; Cannon and Garner, 1999; Casal et al., 1997; Champion et al., 2002; Daggupaty and Sellers, 1990; Donaldson et al., 1987; Gloster et al., 1981; Lee et al., 2014; Seo et al., 2014; Traulsen et al., 2010).

Several other papers describe the application of dose-response models (see Section 3 and Appendix A). With respect to FMDV, the binomial model was used in two Australian studies (Garner et al., 2006; Hess et al., 2008), with the single-hit probability being equal to 0.031 (cattle), 0.045 (sheep), and 0.003 (pigs). The exponential model was used for risk assessment of burning carcases (Jones et al., 2004) and for analyses of the 1966-1968 and 2007 epizootics in the UK (Sanson et al., 2011; Schley et al., 2009).

For B. anthracis an empirical age-dependent model was incorporated in an emergency response model (Wein et al., 2003) (although empirical models lack biological physical basis, so they cannot be extrapolated to domains outside the domain of interest):

Pinf (D, a) = $(y + 5 log (D) + ea + Z a2) (10)

where $ is the standard normal cumulative distribution function, and a is the age [years]. An alternative age-dependent model was used in a follow-up paper (Craft et al., 2005):

Pnf(D, a) = rnrn^, cJLa)

Stuart and Wilkening (2005) and Isukapalli et al. (2008) used comparable functions in their anthrax emergency preparedness models.

12 J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21

Ssematimba et al. (2012) applied an exponential logit model to estimate the risk of AIV infections:

Pinf (D) = [1 + exp (n + к • D]-1 (12)

where n and к are shape parameters.

Finally, Dowd et al. (2000) used the exponential (rotavirus and coronavirus) and beta-Poisson model (Salmonella sp. and E. coli) in their biosolids risk assessment. Dungan (2014) applied the beta-Poisson model to dispersion of C. jejuni, E. coli, L. monocytogenes, and Salmonella sp. Stellacci et al. (2010) applied it to their risk assessment of Cryptosporidium, Campylobacter and rotavirus dispersion from a WWTP.

7. Conclusions

In this review we discussed studies modelling the dispersion of bioaerosols that are pathogenic to humans and animals, with a special focus on risk assessment. The choice for a specific type of atmospheric dispersion model (ADM) - Gaussian, Eulerian, or Lagrangian - depends on the spatial scale of interest, the complexity of the analysis, and one's preference for forward or backward analysis. For instance, Gaussian plume models neglect the heterogeneity of a complex wind field, while models based on computational fluid dynamics (CFD) simulate dispersion at a high three-dimensional resolution.

Transmission routes included human-human, livestock-livestock, livestock-human, and industrial-human. Short-range (several kilometres) transmission was indicated in many studies, however, solid evidence for long-range (tens to hundreds of kilometres) transmission was not found. In order to predict exposure levels as accurately as possible, high-resolution data on wind speed, wind direction, atmospheric stability, and topography are essential for dispersion modelling, and humidity, temperature, and ultraviolet radiation are crucial for modelling inactivation.

Parameterisations for re-aerosolisation were not included in the studies reviewed, although re-aerosolisation could result in additional exposure. That is, it may occur particularly in case of a high degree of contamination of the environment. As a result, additional (environmental) sources may contribute to the total exposure.

In addition, we have not found studies with quantified and substantiated choices for a specific particle size distribution profile (although several ADMs have included options for a particle size distribution, the choice for a specific profile is crucial. For instance, virus particles are much smaller and lighter than spores and are thus they are transmitted much further (when neglecting inactivation).

A major drawback of a majority of the studies was the lack of quantitative analyses and application of a full quantitative micro-bial risk assessment (QMRA) (including dose-response functions). In particular, (qualitative) conclusions solely based on dispersion maps, threshold doses and expert judgement were frequently encountered.

Examples of full emergency preparedness models were only found in B. anthracis dispersion studies. To improve risk assessment for other outbreaks and releases, it is highly recommendable developing such models for other pathogens as well. They would not only include an ADM, but also (1) well-quantified emission and inactivation rates, (2) estimated doses based on exposure duration, breathing rate, lung volume, and particle size distribution, and (3) dose-response models to estimate infection probabilities. Inactivation and emission rates are crucial, and should be quantified whenever possible. Then, such full risk assessment models will not only estimate the areas at risk qualitatively, but also quantify the expected health outcome in the human population or livestock farms.

Declaration of interest statement

Acknowledgements

None. Appendix A

Table A.1. Appendix B

Scopus search query (d.d. October 10, 2014).

TITLE-ABS-KEY((((atmospheric* OR airborne OR aerial) W/3 (model* OR dispersion OR dispersal OR simulate)) OR (predict* W/3 spread*) OR (wind* W/3 (model* OR simulat*)) OR "gaussian puff' OR "gaussian plume" OR (plume W/3 model*) OR (puff W/3 model*) OR lagrangian OR euler* OR cfd OR "computational fluid dynamic*" OR computational-fluid-dynamic*) AND (pathogen* OR microorganism* OR "micro-organism*" OR microbial* OR bioaerosol* OR "bio-aerosol*" OR "viable aerosol*" OR *virus* OR *bacter* OR *fever* OR *virinae* OR *viridae* OR *microbium* OR *microbia* OR fungus OR fungi OR zo$notic* OR zo$nos* OR endotoxin* OR *spore* OR esbl "a(h1)" OR "a(h1n1)" OR "a(h3)" OR "a(h5)" OR "acquired immunodeficiency syndrome" OR "acute respiratory infection" OR "bloodstream infection" OR "bovine spongiform encephalopathy" OR "bubonic plague" OR "carneocephallus brevicaea" OR "creutzfeldt jakob" OR "e. coli" OR "fibricola seoulensis" OR "foot and mouth" OR "genital wart" OR "gongylonema pulchrum" OR "loboa loboi" OR "lymphogranuloma venereum" OR "meticillin resistant staphylococcus aureus" OR "multiple eschars" OR "rift valley" OR "toxic shock syndrome" OR "west nile" OR *bacill* OR *encephalit* OR *herpes* OR *influenza* OR *meningitis* OR *tubercul* OR abiotrophi* OR absettarov OR absidi* OR acanthamoeb* OR acanthocephal* OR acanthopodid* OR achillurban* OR acidaminococc* OR aconoidasis* OR acremon* OR acrophialophora* OR actinomadur* OR actinomyc* OR aedes OR aerococc* OR aeromon* OR aeromonad* OR afipi* OR agarical* OR agaricomyc* OR agrococc* OR agromyc* OR ajellomyc* OR alaria* OR alcaligen* OR alternar* OR amapar* OR amoebid* OR amoebozo* OR amphimer* OR amycolatops* OR anaerococc* OR anaplasm* OR anatrichosom* OR ancylista* OR ancylostom* OR angiostrongyl* OR anisaki* OR anoplocephalid* OR anoplur* OR anseriforme* OR anserina* OR anthrax OR aonchothec* OR aphanoasc* OR apiospor* OR apophall* OR apophysomyc* OR arachnomyc* OR archamoeb* OR archea OR archiacanthocephal* OR arthrin* OR arthrini* OR arthroderm* OR artiodactyl* OR artyfechinostom* OR ascari* OR ascocotyl* OR ascomycot* OR aspergill* OR asthma OR astigmat* OR aureobasidi* OR aureobasidi* OR austrobilharzi* OR babesi* OR balamuthi* OR balantidi* OR bartonell* OR basidiobol* OR basidiomyc* OR basipetospor* OR baylisascar* OR beauveri* OR bergeyell* OR bertiell* OR bilharziell* OR bilophil* OR bipolari* OR blastocyst* OR blastomyc* OR bluetongue OR bolbosom* OR bordetell* OR borreli* OR bosea OR botryomyc* OR botryosphaeri* OR botryt* OR botulism OR brachyspir* OR brevundimon* OR brucell* OR burkholder* OR candida* OR capnocytophag* OR cathaemasi* OR cedece* OR cellulomon* OR centrocest* OR cephaliophor* OR cephalospor* OR cephalotrich* OR cercospor* OR cerinoster* OR chaetomi* OR chaetophom* OR cheilospirur* OR chikungunya OR chiroptera* OR chlamyd* OR chlamydophil* OR chlorococcal* OR chloroflex* OR choanephor* OR choanozo* OR cholera OR chromelospor* OR chroococc* OR chryospor* OR chryseomon* OR chrysonil* OR chrysospor* OR ciliophor* OR citrococc* OR cladophialophor* OR cladorrhin* OR cladospor* OR clavicipit* OR clavispor* OR clinostom* OR clinostomatid* OR clonorch* OR clostrid* OR coccidioid* OR coccodin* OR cochliobol* OR cokeromyc* OR coleophom* OR colletotrich* OR collinsell* OR comamona* OR conidiobol* OR coniochaet* OR coniothyr* OR conoidasid* OR contracaec* OR coprin* OR cordyc* OR corynespor*

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Overview of all atmospheric pathogen dispersion studies discussed in this review, including their main characteristics: pathogen, study type (hypothetical outbreak, model analysis, outbreak, simulation, simulation and measurements), country and year of investigation, pathogen source, model type and model name, meteorological data, inclusion of deposition (Dep.) and particle size distribution (PSD), trajectory type (B = backward, F = forward), emission parameterisation, inactivation parameterisation, and type of dose response model used. (#) = unspecified, (-) = not relevant, (@) = assumptions. All abbreviations are explained in Table 1.

Paper Pathogen Type Country of investigation Year Source(s) Model type (model name) Meteorological data (institute) Emission data Dep. PSD Traj. Inactivation Dose-response model

Blackall and FMD< Outbreak UK 1966-1967 Cattle, pigs, Gaussian dispersion Local station(s) (Met Yes (#) - -- - Inactivation for RH Threshold (#)

Gloster (1981) sheep equation Office) < 60%

Blatny et al. L pneumophila Simulation and Norway 2006 Wastewater CFD (ANSYS-Fluent) An unspecified NWP (#) # # - - -

(2008) measurements (NMI)

Blatny et al. L pneumophila Simulation and Norway 2007 Wastewater CFD (ANSYS-Fluent) In situ Yes (#) Yes Yes F - -

(2011) measurements

Buckeridge B. anthracis Hypothetical USA 2001-2003 Urban Puff(HPAC) Local station(s) (#) 1 kg, 0.1 kg, 0.01 kg # # - - Probit model

et al. (2006) outbreak (Glassman, 1966)

Cannon and FMD< Hypothetical Australia 1940-1995 Cattle, pigs, Gaussian dispersion Stations across 1.8e5 (cattle), 1.5e5 Yes Yes - Inactivation for RH Binomial model:

Garner(1999) outbreak sheep equation Australia (ABM) (sheep), 2.8e8 (pigs) [IU/day] < 60% or > 27 °C r = 0.03 (cattle) and 0.06 (sheep)

Casal et al. FMD< Outbreak UK 1967 Pigs Plume (ALOHA) @ 4e3 (pig), 85 (cattle), - - - Inactivation for RH #

(1995) 66 (sheep) [ID50/min]. Farm-level: 16e3 ID50/min < 60%

Casal et al. FMDV, PR< Outbreak UK, USA 1967, Cattle, pigs Plume (ALOHA) # FM5D0V: 5.1 (cattle) - - - Inactivation for RH Threshold: FMDV:

(1997) 1981-1982, 1988 and 6.8 (pigs) [log10 TCID50/animal/day]. < 55% 10 (cattle) and 400 (pigs) TCID50/m3.

PRV: 5.3 log10 PRV: 1 TCID50/m3

TCID50/animal/day

Champion et al. FMD< Outbreak UK 2001 Burning of Puff (NAME) Unified model (Met 6.5 log10 TCID50 Yes Yes - - Threshold: 0.06 TCID50/m3

(2002) animal carcasses on open pyres Office) per pyre during 3 hours

Craft et al. B. anthracis Model analysis USA - Urban Plume (Wein et al., @ 1e15 spores (1 kg) - - - - Age-dependent

(2005) 2003) model: P(D, a) = mln (1- TJ-JU ^ a = age [years] [c1 = 38,000 and c21 = 450]

Daggupaty and FMD< Outbreak Canada 1951-1952 Cattle, pigs, Plume (#) # (Canadian Climate 3.23e3 (pigs), 1.98 - - - Inactivation for RH T2hreshold: 1 IU

Sellers (1990) sheep Center) (cattle, sheep) [IU/s] < 60%. Temperature was always < 2.8 °C (cattle)

Donaldson FMD< Outbreak France, UK 1981 Cattle, pigs Plume (Gloster et al., Local station(s) (Met # - - - - Threshold: 1 and

et al. (1982) 1981) Office) 0.01 IU(cattle)

Dowd et al. Rotavirus, Simulation with USA 1995 Biosolids from Gaussian dispersion Sensitivity analysis Point source: - - - Rates: 2.86e-2 Viruses: exponential

(2000) Coronavirus, Salmonella sp., E. coli measurements from previous work (Dowd et al., 1997) wastewater equation 1.974e6 (Sa/moneZZa), 27 (virus). Area source: 5.11e6 (Salmonella), 750 (virus) (rotavirus), 2.66e-2 (coronavirus), 2.35e-4 (SQZmoneZZQ sp.), 1.92e-4 (Escherichia coli) model [r = 39.5]. Salmonella sp.: beta-Poisson model [a = 23,000, P = 0.3126]

Dungan (2014) C. jejuni, E. coli (O157:H7 and non-O157), L monocytogenes, Simulation USA 2000-2004 Wastewater Plume (AERMOD) Local stations(s) (NOAA), MM5 27-3.2e6 cells/s Yes Yes Rate: 0.002 and 0.07 s-1 Beta-Poisson model for C. jejuni [a = #; P = #], E. coli O157:H7

Fossum et al. (2012) Gao et al. (2008) Garner et al. (2006)

Gloster et al. (1981)

Salmonella spp.

L pneumophila SARS virus FMD<

Simulation and

measurements

Simulation and

measurements

Hypothetical

outbreak

Norway

China (Hong

Australia

2003-2004

Wastewater Urban

Cattle, pigs, sheep

Cattle, pigs, sheep

CFD (ANSYS-Fluent) In situ CFD (ANSYS-Fluent) @

Particle mode (HYSPLIT)

Gaussian dispersion equation

LAPS (ABM)

Local station(s) (Met Office)

Modified version of an intra-farm virus model (James and Rossiter, 1989) 8 (pigs), 5 (cattle, sheep) [log10 IU/animal/day]

Yes (#)

Rate: 6.4e-4 * 0.5 h-1

Inactivation for RH

[a = 0.0571 ;

ß = 2.2183], E. coli

non-O157 [#], L

monocytogenes

[a = 0.49;

N50 = 5.96e5], and

Salmonella sp. [#]

Exponential model

Binomial model: r = 0.031 (cattle), 0.045 (sheep), 0.003 (pigs)

Threshold: 1 IU

¡=¡ pj

O) h 3

ró" 2.

2 o BJ

i—- TJ

¿i Q. X h.

çjS_ g

Table A.l (continued)

Paper Pathogen Type Country of Year Source(s)

_investigation___

Gloster 19*3 Newcastle disease Outbreak DK 19b9 Poultry

virus Gloster. 1981)

Gloster et al. (1984) Gloster et al. (2001)

Gloster et al.

(2003)

PRV FMDV

Outbreak Outbreak

1981-1982 2001

Burning of animal carcasses on

open pyres

Gloster et al.

(2005a)

Cattle, pigs, sheep

Puff (NAME )

Gloster et al (2005b)

Plume (ADMS)

Gloster et al. (2010)

Grant et al.

(1994)

Cattle, pigs

Puff and Particle mode (CALPUFF, HYSPL1T, M LCD, LODI, NAME, RIMPUFF)

Gaussian dispersion equation

Hess et al. FMDV

(2008)

Hypothetical outbreak

Particle mode (HYSPLIT)

Holden and #

Babcock(1985)

Simulation and measurements

Isukapalli et al. B.anthracis

(2008)

Hypothetical outbreak

Puff (CALPUFF)

Kritana et al.

(2014)

Lee etal. (2014) AIV

Outbreak South Korea 2010-2011 Cattle, pigs Puff (CALPUFF)

Outbreak South Korea 2008 Poultry CFD (ANSYS Fluent)

Li etal. (2013) # (mesophilic bacteria)

Simulation and measurements

Plume (Dowd et al., 2000)

Lighthart and Frisch (1976)

Model analysis

Gaussian plume (equations)

Meteorological data (institute)

Local station(s) (Met Office)

Local station(s) (#)

Unified model (Met Office)

Local station(s) (Met Office), Unified Model, HiRLAM

Local station(s), Unified Model (both Met Office)

Local station(s) (Met Office)

Local station(s) (Met Office), unspecified NWP's

Local station(s) (#)

LAPS (ABM)

# (NOAA)

Emission data

PSD Traj.

Weather Research and Forecasting model

Local station(s) (KMAA)

Proportional to the morbidity, severity and duration of the

disease in a flock #

6.51og10TCID50

per pyre

(Alexandersen et al.. Yes 2003)

(Alexandersen et al.. Yes 2003)

Virus model (Sorenson et al„ 2000)

Varying: max. 8 log10 TCID50/day

5.0-6.3 log10 TCID per herd per day

Modified version of an intra-farm virus model (James and Rossiter. 1989) 22.234 [<2 m/s wind speed]; 22,127 [2.1-5.9 m/s]; 19.556 ]>6 m/s] [particles/s]

(a) 100 g/1 h (b) 100 g/10 h (100 g lel2 spores)

Dose-response model

4.3 (cattle) and 6.1 (Pigs) log10 TCID5Q/animal/day PMjQ-conc. as proxy: 3.6e3 (broiler house) and 116.4 (road) [pg/m3] corrected for bird numbers and stable volumes 3.2722e7 CFU/s

Inactivation for RH

< 60?; or > 21 °C

Inactivation for RH

< 60?; or > 27'C. moderate survival for 20-27 'C. Inactivation for RH

Yes (#)

Inactivation for RH < 25?; RH. linear inactivation function for RH 25-85?; Rate: 6.4e-4 t 0.5 h"1

Yes (#)

Inactivation for RH

< 60?; or > 30'C

Rates: 0.0.4.0e-3. 6.0e-3,0.02. and

Rates: 0.0.1,0.01, 0.001 s"1

Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3 Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3 Threshold: 0.06 TCID50/m3 (cattle)

Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3 Threshold: 0.06 TCID50/m3 (cattle)

Binomial model: r= 0.031 (cattle). 0.045 (sheep). 0.003 (Pigs)

Other DR-models (Craft et al.. 2005. Wein et al.. 2003) and: (I)P(D, o) = i>(a+filog(D)) [a = -2.6361, /i = 0.291 ] (variation 1). [a = 5.6263. /i = 0.621 ] (variation 2. ~ ID50 = 8600 spores) (II) P(D, o) = exp(-^) [0=O.lO9/day, i. = 8.8e-8 s-^ ] (III) P(D, a) = /Mexp(g)-l)

20 pg/mJ

Risk = dose /reference dose. Dose is based on breathing patterns. Reference dose: 1000 CFU/m3

2 Q* ttJ

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TD D ^ CfQ

"S S, .X cr

O CfQ —>■ CD ai p

Table A.l (continued)

Pathogen

Country of Year investigation_

Source(s)

Lighthart and Möhr (1987)

Linetal. (2014)

Liu and You (2012) Low et al.

(2007)

Mar agon et al. (1994)

Mayer et al.

(2008)

Reovirus,

Venezuelan Equine Encep halomyeli tus virus Fusarium

Human influenza virus

Clostridium, Chloroflexi sp., Euryarchaeota FMDV

Simulation and measurements Simulation

Simulation and measurements

Outbreak,

hypothetical

outbreak

Hypothetical

outbreak

Biosolids

Cattle, pigs

Cattle, pigs, sheep

Plume (Lighthart and Frisch, 1976)

Puff (ICAIR3 V)

Trajectories (equations)

Meselson et al. (1994)

Mikkelsen et al.

(2003)

B. anthracis FMDV

Outbreak Outbreak

Russia (former 1979

Soviet-Union)

UK 2001

Military Cattle, pigs

Plume (#)

Moutou and Durand (1994) Nguyen et al. (2006) Nicogossian et al. (2011) Nygärd et al. (2008) Peters on and Lighthart (1977) Rubel and Fuchs(2005)

L pneumophila B. anthracis L pneumophila

Outbreak

Outbreak

Hypothetical

outbreak

Outbreak

Model analysis

Hypothetical outbreak

France, Italy, UK

France

Norway

1981-1982, 1993

2003-2004 #

Cattle, pigs, sheep

Cooling tower Urban Wastewater Cooling towers

Puff (ICAIR3 V)

Plume (ADMS)

Particle mode (OMEGA) Puff (INPUFF)

Sanson et al. (2011) Sauter et al. (2011)

Schley et al. (2009)

Seo and Lee (2013)

Seo et al. (2014)

FMDV C burnetii

AIV AIV

Outbreak Outbreak

Outbreak Outbreak

Netherlands

South Korea South Korea

1967-1968 2008-2009

2008 2008

Pigs Goats

Cattle, pigs, sheep

Poultry Poultry

Puff (NAME) Plume (OPS-ST)

Puff (NAME)

Smith et al. Various pathogens Simulation and USA 2011 #

(2012) measurements

Smith et al. Various pathogens Simulation and USA 2011 #

(2013) measurements

Sorber et al. Escherichia, Simulation and USA 1974 Wastewater

(1976) Klebsiella, Enterobacter, Streptococcus measurements

S0renson et al. FMDV Outbreak Denmark, 1981,1982 Cattle, pigs,

(2000) France, sheep

Trajectories (HYSPLIT) Trajectories (HYSPLIT)

Gaussian dispersion equation

Puff (RIMPUFF)

Germany, UK

S0renson et al. (2001)

Denmark, France, Germany, UK

Cattle, pigs, sheep

Puff (RIMPUFF)

Sprigg et al. (2014)

C. immites, C. posadasii

Eulerian (DREAM)

Meteorological data (institute)_

Emission data

Local station(s) (Oregon State University)

Dose-response model

100 particles/m^/s

Function of RH, T, radiation and time

In situ

Local station(s) (#)

2.1 e5 particles/room

Back-calculated from concentration measurements 71og10 ID50/day

Lokal-Modell-

Kürzestfrist

Local station(s) (NCAR)

Local station(s) (Met Office), Unified Model', HiRLAM

Local station(s) (#) Yes (#) Yes (#)

Local station(s) (NMI)

Yes: 5.06 (cattle). 7.16 (pigs) 4.94 (sheep) [log10

TCID5Q/animal/day]

Virus model (Sorenson et al„

2000) #

10 kg with 106 spores/mg 100 g/s

0.062-0.18 m3/s

Yes Yes Yes Yes

Yes Yes

Rate: 0.001 min-(or 1.67e-5 s-1 )

Inactivation for RH

Rates: 0.0001,0.001,

Threshold: 0.06 (cattle), 1.11 (sheep), 7.70 (pigs) TCID50/m3

Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3

Attack rate analysis

Unspecified model (DWD)

Local station(s) (Met Office)

Local station(s) (KNMI)

Local station(s) Unified Model (both Met Office)

Yes (#)

# (KMAA)

8.6 log10

TCID5Q/animal/day 0-6 log10

TCID5Q/animal/day Three

time-dependent emission profiles

Yes (#)

PMjg-concas proxy: 3.6e3 (broiler house) and 116.4 (road) [pg/m3], corrected for bird numbers and stable volume

Inactivation for RH

< 55?; or > 27'C.

Yes Yes

Yes Yes

Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3

Yes (#)

Exponential model

Threshold:

20 pg/m3

In situ, local station(s)

Local station(s) (#), HiRLAM

3.5-4.7 (cattle).

4.3-8.6 (pigs).

2.4-5.1 (sheep) [log10

TCID^g/animal/day] Virus model (Sorenson et al., 2000)

Inactivation for RH < 55?;. Rate for RH >55?;:3.2e-4h-1 ss 8.9e-8 s-1

Inactivation for RH

NCEP/NCAR, ECMWF Yes (#)

Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3

Threshold: 0.06 (cattle). 1.11 (sheep). 7.70 (pigs) TCID50/m3

C O) i 3 e t

o ■a

p 5' i/

O oq ^ a>

OJ 3 D.

Table A.1 (continued)

Paper Pathogen Type Country of investigation Year Source(s) Model type (model name) Meteorological data (institute) Emission data Dep. PSD Traj. Inactivation Dose-response model

Ssematimba AIV (H7N7) Outbreak The 2223 Poultry Gaussian dispersion Local station(s) 0.0122 g Yes - - Rates: Exponential model

et al. (2012) Netherlands equation (KNMI) dust/animal/h 4.0e-7-2.0e-6s-1 (variation): Pinf (D) = [1 + exp(a + y • D]-1 [a = 4.67, y = -1 87]

Stellacci et al. Cryptosporidium, Simulation Italy # Wastewater Plume (GIADA) Yes (#) 57.87 oocysts/s # # - Rate: 0.1 s-1 Beta-Poisson model:

(2010) Camipylobacter, rotavirus (Cryptosporidium), 578.7 CFU/s {Campylobactet), 587.7 MPN/s (rotavirus) Crypyystoridium [a = 0.115, p=0.176, D50=73], Campylobacter [a = 0.024; P = 0.011; D50 = 3.84e10], rotavirus [a = 0.2531; P = 0.4265;

1015 spores (~1 kg) d50 = 6."]

Stuart and B. anthracis Hypothetical - - Urban Gaussian dispersion @ - - - Four models Probit model:

Wilkening outbreak equation (sensitivity analysis) pdeath <x- =

(2005) ¿»Tfeexp[-Xr]d-

Taha et al. A.fiimimLitiaa, Simulation and UK # Biosolids Plume (SCREEN3) In situ 3.6e3- - - - -

(2005) mesophylic actinomycetes measurements 2.17e4 CFU/m2/s

Taha et al. A. finnimatiis Simulation and UK 2004 Biosolids Plume (SCREEN3) In situ 5e5-8.6e8 CFU/s - - - - -

(2006) measurements

Taha et al. A.fiimimLitiis, Simulation and UK 2005 Biosolids Plume (ADMS, In situ 4.8e4-1.6e7 CFU/s - - - - -

(2007) mesophylic actinomycetes measurements SCREEN3)

Tang et al. B. anthracis Simulation - - Urban CFD (equations) @ - - - B - -

(2009)

Teltsch et al. E. coli Simulation and Israel 1978 Wastewater Gaussian dispersion In situ, local Yes (#) - - - 8.8e-3 s-1 (early -

(1980) measurements equation station(s) (Israelian Meteorological Service) morning), 6.6e-2 s-1 (afternoon)

Traulsen et al. FMDV Outbreak Germany 2003 Cattle, pigs, Plume (RIMPUFF) # Virus model Yes Yes - Rate: Threshold: 0.045

(2010) sheep (Sorenson et al., 6.4e-4 x 0.5 h-1 TCID50

Traulsen and FMDV Outbreak # # Pigs Gaussian dispersion Local station(s) (#) 6.7 log10TCID50/s - - - - Threshold: 0.06

Krieter(2012) equation (pigs) (cattle), 1.11 (sheep),

7.70 (pigs) TCID50/m3

Van Leuken C. burnetii Outbreak The 2009 Goats Plume (OPS-ST) Local station(s) Three Yes Yes - - -

et al. (2015) Netherlands (KNMI) time-dependent emission profiles

Wallensten C. burnetii Outbreak UK 2007 Sheep Puff(NAME) Local station(s), - Yes Yes - - -

et al. (2010) Unified Model (Met

Office) 1015 spores (~1 kg)

Wein et al. B. anthracis Model analysis USA - Urban Gaussian dispersion @ - - - - Age-dependent

(2003) equation probit model: P(D, a) = + p log(D) + ya + < with a = age [years] [a = -9.733, p = 1.025, Y = -0.016/year, and 8 = 6e-4/year2]

Yu et al. (2004) SARS virus Outbreak China (Hong Kong) 2003 Urban CFD (ANSYS-Fluent) Local station(s) (HongKong Observatory)

J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21 17

OR corynosom* OR cowpox OR coxiella* OR creutzfeldt-jakob OR cryptococc* OR cryptocotyl* OR cryptosporid* OR culicid* OR culicin* OR cunninghamell* OR curvular* OR cyclophyllid* OR cyclospor* OR cylindrocarpon* OR davaineid* OR davidiell* OR deinococc* OR delfti* OR dendrocygnin* OR dendryphi* OR dengue OR dermatophil* OR dermocystid* OR desulfovibrion* OR diarrhoea OR dichotomophthor* OR dicrocoel* OR dientamoeb* OR dietzi* OR dilepidid* OR dinemaspor* OR dioctophym* OR dipetalonem* OR diphtheria OR diphyllobothr* OR diplococc* OR diplogonopor* OR diplostom* OR dipodasc* OR diptera* OR dipylid* OR dirofilar* OR dissitimur* OR doratomyc* OR dothid* OR dothior* OR dracuncul* OR drechsler* OR drepanidotaen* OR duganell* OR dysentery OR ebola OR echinochasm* OR echinococc* OR echinoparyph* OR echinostom* OR edwardsiell* OR eggerthell* OR ehrlichi* OR eikenell* OR eimeriid* OR emericell* OR emmonsi* OR encephalitozoon* OR engyodonti* OR enoplid* OR entamoeb* OR enterob* OR enterococc* OR enterocytozoon* OR entomophthor* OR epicocc* OR epidermophyt* OR episthmi* OR erwini* OR erysipelothr* OR escherich* OR eucoccidiorid* OR eucole* OR euglenoz* OR eupenicilli* OR eurotial* OR eurotiomycet* OR eurytrem* OR eustrongylid* OR ewingell* OR exobasidiomyc* OR exophial* OR exserohil* OR fasciol* OR filifactor* OR filobasidi* OR finegoldi* OR firmicut* OR flavimon* OR fmd OR fonseca* OR foot-and-mouth OR francisell* OR franki* OR fulvimari* OR fusari* OR ganoderm* OR gardnerell* OR gastrodisc* OR gastroenteritis OR gemell* OR geotrich* OR giardi* OR gibberell* OR gigantobilharz* OR glomerell* OR gnathostom* OR gonococcal OR gonorrhoea OR gordoni* OR granulicatell* OR grimont* OR guillain-barre OR guillain-barre OR gymnoasc* OR gymnophall* OR h10n7 OR h1n2 OR h2n2 OR h3n1 OR h3n2 OR h3n8 OR h4n6 OR h5n1 OR h5n2 OR h5n7 OR h7n1 OR h7n2 OR h7n3 OR h7n7 OR h9n2 OR haematonectr* OR haemolytic-uremic OR haemonch* OR haemophil* OR haemorrhagic OR haemosporid* OR hafni* OR halosphaer* OR hansenul* OR hanta OR haplorch* OR helcococc* OR hepatitis OR herpotrichiell* OR heterobilharz* OR heterolobos* OR heteroph* OR hexamitid* OR hib* OR himasthl* OR histoplasm* OR hiv* OR hortae* OR hu39694 OR hymenolep* OR hypocrea* OR hypoder* OR hyponectr* OR hysteri* OR inermicapsifer* OR isaria OR isoparorch* OR isospor* OR issatchenk* OR jiangell* OR kineococc* OR kinetoplast* OR kingell* OR klebsiell* OR kluyver* OR kocuri* OR kurthi* OR lachnospir* OR lagochilascar* OR lasiodiplodi* OR lasiosphaer* OR lechevalieri* OR lecithodendriid* OR lecythophor* OR legionell* OR legionnaires* OR leifson* OR leishman* OR lentz* OR leptosphaer* OR leptospir* OR leptotrich* OR lewia* OR libertell* OR ligula* OR listeri* OR lithothel* OR litostomat* OR lojkan* OR lophiostom* OR lyngby* OR macracanthorhynch* OR madurell* OR magnaporth* OR malaria OR malassez* OR mammomonogam* OR mannheimi* OR mansonell* OR marshallag* OR massar* OR massili* OR mathevotaen* OR measles OR mecistocirr* OR megamon* OR megasphaer* OR melanommat* OR memnoniell* OR meningococc* OR meningonem* OR mergina* OR mesocestoid* OR mesomycetoz* OR mesorhizobi* OR metagonim* OR metamonad* OR metastrongyl* OR methanosarcinal* OR methanosphaer* OR metorch* OR metschnikow* OR microasc* OR microbotryomyc* OR micrococc* OR microdochi* OR microfilar* OR micromon* OR micronem* OR microspor* OR microstromatal* OR molineid* OR mollicut* OR moniez* OR monili* OR moniliell* OR moniliform* OR monocercomonadid* OR monographell* OR mononegaviral* OR moraxell* OR morganell* OR mortierrell* OR mrsa OR mucor* OR multiceps* OR mumps OR mycel* OR myceliophthor* OR mycocentrospor* OR mycoleptodisc* OR mycoplasm* OR mycosphaerell* OR myositis OR myriang* OR myriodont* OR myroid* OR myxotrich* OR myzoz* OR naegler* OR nannizz* OR nanophyet* OR nattrass* OR necator OR nectria* OR neisser* OR neocosmospor* OR neodiplostom* OR neoricketts* OR neosartory* OR neotestudin* OR neurospor* OR newcastle*

OR nidovir* OR nigrospor* OR nitrosomonadal* OR nocard* OR nosem* OR nostoc OR novosphingobi* OR ochrobactr* OR ochrocon* OR oerskov* OR oesophagostom* OR oidiodendr* OR oidium OR oligacanthorhynchid* OR oligell* OR onchocerc* OR onychocol* OR onygen* OR oomyc* OR ophiostomat* OR opisthorch* OR orientia OR ornithobilharz* OR ostertag* OR ovadendr* OR oxyurid* OR oxyurin* OR paecilomyc* OR palaeacanthocephal* OR panagrolaimid* OR pan-toea OR papular* ORparacocc* OR paragonim* ORparamphistomid* OR parascar* OR parastrongyl* OR paratyphoid OR pasteurell* OR pasturell* OR pearsonem* OR pedicul* OR pelliodit* OR pelobiontid* OR peloder* OR penicill* OR pentatrichomon* OR peptococc* OR peptoniphil* OR peptostreptococc* OR percoloz* OR pericon* OR pericon* OR pertussis OR phaeoannellomyc* OR phaeoscler* OR phaeosphaer* OR phaeotrichocon* OR phaneropsol* OR phialemon* OR phialophor* OR philophthalm* OR phocanem* OR phoma OR phoma* OR phormidi* OR phthirus OR phyllostict* OR physalopter* OR pichia* OR piedrai* OR piroplasmid* OR plagiorch* OR plague OR planctomyc* OR planococc* OR plasmod* OR platyhelminth* OR plectropterin* OR pleospor* OR plesiomon* OR pleurophom* OR pneumococc* OR pneumocyst* OR pneumoni* OR poikilorch* OR poliomyelitis OR porphyromon* OR prevotell* OR procerov* OR prohemistom* OR promicromonospor* OR prosthodendr* OR proteus OR protothec* OR protozoa OR providenc* OR pseudallescher* OR pseudamphistom* OR pseudocochliobol* OR pseudomicrodoch* OR pseudomon* OR pseudonocard* OR pseudophyllid* OR pseudoterranov* OR psilorch* OR psilostomatid* OR psittacosis OR pygidiops* OR pyramicocephal* OR pyrenochaet* OR pythia* OR pythium OR pythomyc* OR quambalar* OR rabies OR rahnell* OR raillietin* OR ralstoni* OR raoulterr* OR retortamon* OR rhabdit* OR rhinocladiell* OR rhinosporid* OR rhizobial* OR rhizomucor* OR rhizop* OR rhodococc* OR rhodotorul* OR rickettsi* OR rictular* OR rochalim* OR rothia OR rubella OR ruminococc* OR saccharomonospor* OR saccharomyc* OR saccharopolyspor* OR saccharothr* OR saksena* OR salmonell* OR sarcin* OR sarcinomyc* OR sarcocyst* OR sarcopt* OR sars OR scabies OR scarlet OR scedospor* OR schistocephal* OR schistosom* OR schizophyll* OR schizopyrenid* OR schizothr* OR sclerot* OR scolecobasid* OR scopulariops* OR scytalid* OR scytonem* OR sebaldell* OR secernent* OR selenomon* OR serpulin* OR serrati* OR setari* OR setosphaer* OR shewanell* OR shigell* OR silicosis OR smallpox OR sordaria* OR sordariomyc* OR sphingomon* OR spirill* OR spirocerc* OR spirochaet* OR spirometr* OR sporidiobolal* OR sporothr* OR stachybotr* OR staphylococc* OR stellantchasm* OR stemphyl* OR stenotrophomon* OR stictodor* OR stictonettin* OR stomatitis OR streptococc* OR streptomyc* OR strigeid* OR strongyl* OR sutterell* OR suttonell* OR syncephalastr* OR syngamid* OR syphac* OR syphilis OR syphillis OR taeni* OR tannerell* OR tatlocki* OR tatumell* OR teladorsagi* OR ternidens* OR testudin* OR tetanus OR tetraploa* OR thalassornin* OR thamnidi* OR thermoactinomyc* OR thermomonospor* OR thermomyc* OR torul* OR toxocar* OR toxoplasm* OR trachipleistophor* OR trebouxiophy* OR trematod* OR tremell* OR trepomonad* OR treponem* OR trichinell* OR trichobilharz* OR trichocephalid* OR trichocom* OR trichoderm* OR trichomar* OR trichomon* OR trichophyt* OR trichosphaerial* OR trichospor* OR trichostrongyl* OR trichuri* OR triotrichal* OR tritirach* OR troglotrematid* OR tropherym* OR trypanosom* OR tsukamurell* OR tubulin* OR tularaemia OR typhus OR uloclad* OR ureaplasm* ORustilaginomycotin* ORvahlkampfiid* OR varicella OR veillonell* OR verona* OR verruco* OR verticill* OR vestibuliferid* OR vibrio* OR vittaform* OR volutell* OR wallemi* OR watsoni* OR west-nile OR wolinell* OR wucherer* OR xanthamon* OR xanthomon* OR xylarial* OR yarrowia OR yersini* OR zoogl* OR zygomyc* OR zygospor*) AND NOT (indoor OR hospital* OR phytoplankt* OR biomass OR membrane* OR genes OR cardia* OR "operating room*" OR biofilm* OR cough* OR fiber* OR hypertension OR hypotension OR chamber* OR crop* OR chlorophyll* OR

18 J.P.G. Van Leuken et al./Microbial Risk Analysis 000 (2015) 1-21

candidate* OR window* OR odour OR electric* OR serration* OR "water quality"))

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