Towards improved bioaerosol model validation and verification

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dc.contributor.authorWilliams, Ben
dc.contributor.authorHayes, Enda
dc.contributor.authorNasir, Zaheer A.
dc.contributor.authorRolph, Catherine A.
dc.contributor.authorJackson, Simon
dc.contributor.authorKhera, Shagun
dc.contributor.authorBennett, Alan
dc.contributor.authorGladding, Toni
dc.contributor.authorDrew, Gillian
dc.contributor.authorLonghurst, James
dc.contributor.authorTyrrel, Sean
dc.date.accessioned2020-02-26T15:47:33Z
dc.date.available2020-02-26T15:47:33Z
dc.date.issued2018-10-23
dc.description.abstractBioaerosols, comprised of bacteria, fungi and viruses are ubiquitous in ambient air. Known to adversely affect human health, the impact of bioaerosols on a population often manifests as outbreaks of illnesses such as Legionnaires Disease and Q fever, although the concentrations and environmental conditions in which these impacts occur are not well understood. Bioaerosol concentrations vary from source to source, but specific industrialised human activities such as water treatment, intensive agriculture and open windrow composting facilitate the generation of bioaerosol concentrations many times higher than natural background levels. Bioaerosol sampling is currently undertaken according to the requirements of the Environment Agency’s regulatory framework, in which the collection of bioaerosols and not its long-term measurement is of most importance. As a consequence, sampling devices are often moved around site according to changing wind direction and sampling intervals are invariably short-term. The dispersion modelling of bioaerosols from composting facilities typically relies on proxy pollutant parameters. In addition, the use of short term emission data gathering strategies in which monitors are moved frequently with wind direction, do not provide a robust reliable and repeatable dataset by which to validate any modelling or to verify its performance. New sampling methods such as the Spectral Intensity Bioaerosol Sensor (SIBS) provide an opportunity to address several gaps in bioaerosol model validation and verification. In the context of model validation, this paper sets out the current weaknesses in bioaerosol monitoring from the perspective of robust modelling requirements.en_UK
dc.identifier.citationWilliams B, Hayes E, Nasir Z, et al., (2018) Towards improved bioaerosol model validation and verification. In: Air Pollution XXVI : Air Pollution 2018, 19-21 June 2018, Naples, Italy; WIT Transactions on Ecology and the Environment, Volume 230, pp. 41-50en_UK
dc.identifier.isbn978-1-78466-269-1
dc.identifier.urihttps://doi.org/10.2495/AIR180041
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15189
dc.language.isoenen_UK
dc.publisherWIT Pressen_UK
dc.subjectbioaerosolsen_UK
dc.subjectmodel validationen_UK
dc.subjectverificationen_UK
dc.subjectdispersion modellingen_UK
dc.subjectmonitoringen_UK
dc.titleTowards improved bioaerosol model validation and verificationen_UK
dc.typeConference paperen_UK

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