Relationships between airborne pollen grains, wind direction and land cover using GIS and circular statistics

A - Papers appearing in refereed journals

Maya-Manzano, J. M., Sadys, M., Tormo-Molina, R., Fernandez-Rodriguez, S., Oteros, J., Silva-Palacios, I. and Gonzalo-Garijo, A. 2017. Relationships between airborne pollen grains, wind direction and land cover using GIS and circular statistics. Science of the Total Environment. 584–585 (15 April), pp. 603-613.

AuthorsMaya-Manzano, J. M., Sadys, M., Tormo-Molina, R., Fernandez-Rodriguez, S., Oteros, J., Silva-Palacios, I. and Gonzalo-Garijo, A.
Abstract

Airborne bio-aerosol content (mainly pollen and spores) depends on the surrounding vegetation and weather conditions, particularly wind direction. In order to understand this issue, maps of the main land cover in influence areas of 10 km in radius surrounding pollen traps were created. Atmospheric content of the most abundant 14 pollen types was analysed in relation to the predominant wind directions measured in three localities of SW of Iberian Peninsula, from March 2011 to March 2014. Three Hirst type traps were used for aerobiological monitoring. The surface area for each land cover category was calculated and wind direction analysis was approached by using circular statistics. This method could be helpful for estimating the potential risk of exposure to various pollen types.

Thus, the main land cover was different for each monitoring location, being irrigated crops, pastures and hardwood forests the main categories among 11 types described. Comparison of the pollen content with the predominant winds and land cover shows that the atmospheric pollen concentration is related to some source areas identified in the inventory. The study found that some pollen types (e.g. Plantago, Fraxinus-Phillyrea, Alnus) come from local sources but other pollen types (e.g. Quercus) are mostly coming from longer distances.

As main conclusions, airborne particle concentrations can be effectively split by addressing wind with circular statistics. By combining circular statistics and GIS method with aerobiological data, we have created a useful tool for understanding pollen origin. Some pollen loads can be explained by immediate surrounding landscape and observed wind patterns for most of the time. However, other factors like medium or long-distance transport or even pollen trap location within a city, may occasionally affect the pollen load recorded using an air sampler.

Year of Publication2017
JournalScience of the Total Environment
Journal citation584–585 (15 April), pp. 603-613
Digital Object Identifier (DOI)doi:10.1016/j.scitotenv.2017.01.085
Open accessPublished as non-open access
Funder project or codeWheat
CP19.1 Modelling Pollen Dispersal in a Hybrid Cereal Canopy
Output statusPublished
Publication dates
Online27 Jan 2017
Publication process dates
Accepted13 Jan 2017
Copyright licenseCC BY
PublisherElsevier
Elsevier Science Bv
ISSN0048-9697

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