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

Leuken, Jeroen P. G. van; Swart, Arno; Havelaar, Arie H.; Pul, Addo van; Hoek, Wim van der; Heederik, Dick

doi:10.1016/j.mran.2015.07.002

Cited by 112 publications

(73 citation statements)

References 196 publications

(288 reference statements)

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“…Our results are consistent with previously published literature on the physical effects of meteorological conditions (e.g., atmospheric stability, wind speed/direction, temperature, and precipitation) on survival and transmission of airborne pathogens [22], [44], [54], [55], [56], and the effects of vegetation, objects, and surface roughness on deposition and reaerosolisation [35]. For instance, precipitation increases the amount of wet deposition, while a smoother surface results in further spread.…”

Section: Discussionsupporting

confidence: 93%

Human Q fever incidence is associated to spatiotemporal environmental conditions

et al. 2016

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Airborne pathogenic transmission from sources to humans is characterised by atmospheric dispersion and influence of environmental conditions on deposition and reaerosolisation. We applied a One Health approach using human, veterinary and environmental data regarding the 2009 epidemic in The Netherlands, and investigated whether observed human Q fever incidence rates were correlated to environmental risk factors.We identified 158 putative sources (dairy goat and sheep farms) and included 2339 human cases. We performed a high-resolution (1 × 1 km) zero-inflated regression analysis to predict incidence rates by Coxiella burnetii concentration (using an atmospheric dispersion model and meteorological data), and environmental factors – including vegetation density, soil moisture, soil erosion sensitivity, and land use data – at a yearly and monthly time-resolution.With respect to the annual data, airborne concentration was the most important predictor variable (positively correlated to incidence rate), followed by vegetation density (negatively). The other variables were also important, but to a less extent. High erosion sensitive soils and the land-use fractions “city” and “forest” were positively correlated. Soil moisture and land-use “open nature” were negatively associated. The geographical prediction map identified the largest Q fever outbreak areas. The hazard map identified highest hazards in a livestock dense area.We conclude that environmental conditions are correlated to human Q fever incidence rate. Similar research with data from other outbreaks would be needed to more firmly establish our findings. This could lead to better estimations of the public health risk of a C. burnetii outbreak, and to more detailed and accurate hazard maps that could be used for spatial planning of livestock operations.

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Section: Discussionsupporting

confidence: 93%

Human Q fever incidence is associated to spatiotemporal environmental conditions

et al. 2016

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“…That might, however, be relevant as outbreaks and releases of pathogens via the airborne transmission route do occur from a variety of sources including livestock, wastewater treatment plants, humans, and industry, either continuously or intermittently (Van Leuken et al 2015a). In this study, we investigated the effects of climate change on airborne pathogen concentrations modelled using an atmospheric dispersion model.…”

Section: Discussionmentioning

confidence: 99%

“…Modelling airborne pathogenic transmission was performed in several studies using atmospheric dispersion models (ADMs) (Van Leuken et al 2015a). ADMs are mechanistic models, initially developed for pollutant dispersion modelling (Holmes and Morawska 2006), and describe the spatial and temporal particle spread as a function of meteorological conditions, such as wind speed, wind direction, temperature, atmospheric stability, global radiation, and humidity.…”

Section: Introductionmentioning

confidence: 99%

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Climate change effects on airborne pathogenic bioaerosol concentrations: a scenario analysis

et al. 2016

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The most recent IPCC report presented further scientific evidence for global climate change in the twenty-first century. Important secondary effects of climate change include those on water resource availability, agricultural yields, urban healthy living, biodiversity, ecosystems, food security, and public health. The aim of this explorative study was to determine the range of expected airborne pathogen concentrations during a single outbreak or release in a future climate compared to a historical climatic period (1981–2010). We used five climate scenarios for the periods 2016–2045 and 2036–2065 defined by the Royal Netherlands Meteorological Institute and two conversion tools to create hourly future meteorological data sets. We modelled season-averaged airborne pathogen concentrations by means of an atmospheric dispersion model and compared these data to historical (1981–2010) modelled concentrations. Our results showed that modelled concentrations were modified several percentage points on average as a result of climate change. On average, concentrations were reduced in four out of five scenarios. Wind speed and global radiation were of critical importance, which determine horizontal and vertical dilution. Modelled concentrations decreased on average, but large positive and negative hourly averaged effects were calculated (from −67 to +639 %). This explorative study shows that further research should include pathogen inactivation and more detailed probability functions on precipitation, snow, and large-scale circulation.

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“…However, neither this modelling attempt nor the sampling campaign within goat farms resulted in quantified emission rates, which are required to predict exposure levels and infection risks based on dispersion models. Therefore, Van Leuken, Swart, et al () defined three simple emission profiles to model exposure (see Section 2.1.3). These included two steady‐state (constant in time) profiles and a log‐normally shaped profile based on the epidemic curves of outbreaks.…”

Section: Quantitative Microbial Risk Assessment Of the Dutch Q Fever mentioning

confidence: 99%

A systematic knowledge synthesis on the spatial dimensions of Q fever epidemics

Rooij

Leuken

Swart

et al. 2018

Zoonoses and Public Health

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From 2007 through 2010, the Netherlands experienced the largest Q fever epidemic ever reported. This study integrates the outcomes of a multidisciplinary research programme on spatial airborne transmission of Coxiella burnetii and reflects these outcomes in relation to other scientific Q fever studies worldwide. We have identified lessons learned and remaining knowledge gaps. This synthesis was structured according to the four steps of quantitative microbial risk assessment (QMRA): (a) Rapid source identification was improved by newly developed techniques using mathematical disease modelling; (b) source characterization efforts improved knowledge but did not provide accurate C. burnetii emission patterns; (c) ambient air sampling, dispersion and spatial modelling promoted exposure assessment; and (d) risk characterization was enabled by applying refined dose–response analyses. The results may support proper and timely risk assessment and risk management during future outbreaks, provided that accurate and structured data are available and exchanged readily between responsible actors.

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

Abstract: 2016-11-02T18:49:00

Cited by 112 publications

References 196 publications

Human Q fever incidence is associated to spatiotemporal environmental conditions

Human Q fever incidence is associated to spatiotemporal environmental conditions

Climate change effects on airborne pathogenic bioaerosol concentrations: a scenario analysis

A systematic knowledge synthesis on the spatial dimensions of Q fever epidemics

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