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Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom

Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom
Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom
Constructing accurate predictive models for grass and birch pollen in the air, the two most important aeroallergens, for areas with variable climate conditions such as the United Kingdom, require better understanding of the relationships between pollen count in the air and meteorological variables. Variations in daily birch and grass pollen counts and their relationship with daily meteorological variables were investigated for nine pollen monitoring sites for the period 2000–2010 in the United Kingdom. An active pollen count sampling method was employed at each of the monitoring stations to sample pollen from the atmosphere. The mechanism of this method is based on the volumetric spore traps of Hirst design (Hirst in Ann Appl Biol 39(2):257–265, 1952). The pollen season (start date, finish date) for grass and birch were determined using a first derivative method. Meteorological variables such as daily rainfall; maximum, minimum and average temperatures; cumulative sum of Sunshine duration; wind speed; and relative humidity were related to the grass and birch pollen counts for the pre-peak, post peak and the entire pollen season. The meteorological variables were correlated with the pollen count data for the following temporal supports: same-day, 1-day prior, 1-day mean prior, 3-day mean prior, 7-day mean prior. The direction of influence (positive/negative) of meteorological variables on pollen count varied for birch and grass, and also varied when the pollen season was treated as a whole season, or was segmented into the pre-peak and post-peak seasons. Maximum temperature, sunshine duration and rainfall were the most important variables influencing the count of grass pollen in the atmosphere. Both maximum temperature (pre-peak) and sunshine produced a strong positive correlation, and rain produced a strong negative correlation with grass pollen count in the air. Similarly, average temperature, wind speed and rainfall were the most important variables influencing the count of birch pollen in the air. Both wind speed and rain produced a negative correlation with birch pollen count in the air and average temperature produced a positive correlation.
hay fever, grass pollen, birch pollen, predicting model, phenology, meteorological variable
529-545
Khwarahm, Nabaz
2e1dea22-1f7f-41d6-b007-ed5bcc95f6ec
Dash, J.
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Newnham, Rewi M.
1f5e245a-a325-4d36-9745-32297e7f9f8c
Skjøth, C.A.
f9f139d4-da17-4888-be7d-bdcedc48884f
Adams-Groom, B.
71cebf81-e12b-4eca-b60f-41e8f6d1256b
Caulton, Eric
de54f322-0b0d-4e3e-8bc1-03b4d2162136
Head, K.
315f3512-82d4-4cd1-bc9f-4497f67db726
Khwarahm, Nabaz
2e1dea22-1f7f-41d6-b007-ed5bcc95f6ec
Dash, J.
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Newnham, Rewi M.
1f5e245a-a325-4d36-9745-32297e7f9f8c
Skjøth, C.A.
f9f139d4-da17-4888-be7d-bdcedc48884f
Adams-Groom, B.
71cebf81-e12b-4eca-b60f-41e8f6d1256b
Caulton, Eric
de54f322-0b0d-4e3e-8bc1-03b4d2162136
Head, K.
315f3512-82d4-4cd1-bc9f-4497f67db726

Khwarahm, Nabaz, Dash, J. and Atkinson, P.M. et al. (2014) Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom. [in special issue: Phenology 2012 Conference, Milwaukee: Future Climate & The Living Planet] International Society of Biometeorology, 58 (4), 529-545. (doi:10.1007/s00484-013-0739-7).

Record type: Article

Abstract

Constructing accurate predictive models for grass and birch pollen in the air, the two most important aeroallergens, for areas with variable climate conditions such as the United Kingdom, require better understanding of the relationships between pollen count in the air and meteorological variables. Variations in daily birch and grass pollen counts and their relationship with daily meteorological variables were investigated for nine pollen monitoring sites for the period 2000–2010 in the United Kingdom. An active pollen count sampling method was employed at each of the monitoring stations to sample pollen from the atmosphere. The mechanism of this method is based on the volumetric spore traps of Hirst design (Hirst in Ann Appl Biol 39(2):257–265, 1952). The pollen season (start date, finish date) for grass and birch were determined using a first derivative method. Meteorological variables such as daily rainfall; maximum, minimum and average temperatures; cumulative sum of Sunshine duration; wind speed; and relative humidity were related to the grass and birch pollen counts for the pre-peak, post peak and the entire pollen season. The meteorological variables were correlated with the pollen count data for the following temporal supports: same-day, 1-day prior, 1-day mean prior, 3-day mean prior, 7-day mean prior. The direction of influence (positive/negative) of meteorological variables on pollen count varied for birch and grass, and also varied when the pollen season was treated as a whole season, or was segmented into the pre-peak and post-peak seasons. Maximum temperature, sunshine duration and rainfall were the most important variables influencing the count of grass pollen in the atmosphere. Both maximum temperature (pre-peak) and sunshine produced a strong positive correlation, and rain produced a strong negative correlation with grass pollen count in the air. Similarly, average temperature, wind speed and rainfall were the most important variables influencing the count of birch pollen in the air. Both wind speed and rain produced a negative correlation with birch pollen count in the air and average temperature produced a positive correlation.

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More information

e-pub ahead of print date: 31 January 2014
Published date: 1 May 2014
Keywords: hay fever, grass pollen, birch pollen, predicting model, phenology, meteorological variable
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 375723
URI: http://eprints.soton.ac.uk/id/eprint/375723
PURE UUID: e9a33ddf-e4a5-4a29-b1f4-551826e68789
ORCID for J. Dash: ORCID iD orcid.org/0000-0002-5444-2109
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 13 Apr 2015 10:30
Last modified: 15 Mar 2024 03:17

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Contributors

Author: Nabaz Khwarahm
Author: J. Dash ORCID iD
Author: P.M. Atkinson ORCID iD
Author: Rewi M. Newnham
Author: C.A. Skjøth
Author: B. Adams-Groom
Author: Eric Caulton
Author: K. Head

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