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Updated: Mar 11, 2026

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
Published on: January 7, 2019
Climate change effects on airborne pathogenic bioaerosol concentrations: a scenario analysis
J P G van Leuken1, A N Swart2, P Droogers3
1Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands ; Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Abstract:
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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