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

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.

Human Q fever incidence is associated to spatiotemporal environmental conditions.

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.

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

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.

Particulate matter strongly associated with human Q fever in The Netherlands: an ecological study.

There are still questions about the importance of different animal reservoirs and environmental factors that played a role in the large Q fever epidemic in The Netherlands. We therefore investigated the spatial association between reported Q fever cases and different livestock and environmental factors at the national level. A spatial regression analysis was performed, with four-digit postal code areas as the unit of analysis. High level of particulate matter (< 24.5 µg/m³) with an aerodynamic diameter <10 µm (PM10) was by far the strongest risk factor for human Q fever with an odds ratio of 10.4 (95% confidence interval 7.0-15.6) using PM10 <24.5 µg/m³ as reference, in logistic regression analysis, controlling for differences in animal densities, vegetation and other risk factors. Particulate matter seems to play an important role in the transmission of Q fever from infected animals to humans and should be a focus for further studies on zoonotic infectious diseases and decision-making.

Detection of Coxiella burnetii DNA in the environment during and after a large Q fever epidemic in the Netherlands.

AIM: To investigate the Coxiella burnetii DNA content in environmental samples that may contribute to the transmission of C. burnetii. METHODS AND RESULTS: During a large Q fever outbreak in the Netherlands, surface swabs and aerosol samples were collected inside stables and around six Q fever-affected ruminant farms, which are located in municipalities varying in Q fever incidence. After the outbreak in 2010, aerosol samples were collected in the same geographical areas. The use of an optimized multiplex qPCR for the detection of C. burnetii DNA revealed that all samples obtained inside stables were positive. In addition, the C. burnetii DNA content in aerosol samples collected in stables is significantly higher than in aerosol samples collected around the farms. Finally, the C. burnetii DNA content in aerosol samples collected in the same geographical locations was lower in 2010 in comparison with 2009. CONCLUSIONS: The reduction in C. burnetii DNA content in aerosol samples between 2009 and 2010 is in agreement with the reduction in Q fever incidence in the same geographical areas. SIGNIFICANCE AND IMPACT OF THE STUDY: The presence of C. burnetii DNA in environmental samples collected on and around ruminant farms supports the hypothesis that C. burnetii can be disseminated from ruminant farms to the surrounding areas.