Literature DB >> 10202342

Regional variations in grass pollen seasons in the UK, long-term trends and forecast models.

J Emberlin1, J Mullins, J Corden, S Jones, W Millington, M Brooke, M Savage.   

Abstract

BACKGROUND: Three sites in the UK have daily records of pollen spanning several decades, giving the longest data sets worldwide. Previous research on London data revealed decreasing severity of grass pollen seasons. This is often taken as a model for the whole country but comparisons with Derby and Cardiff, in different regions of local climate and land-use, emphasize the need for regional studies.
OBJECTIVE: The grass pollen seasons were analysed for three contrasting long-term sites to provide regional insight into the changing incidence of hay fever.
METHODS: Pollen was monitored by volumetric instruments using standard techniques. Data have been taken from 1961 to 1993 to examine variation in grass pollen seasons in relation to land-use changes, cumulative temperatures and rainfall. Models were developed to predict total seasonal catches.
RESULTS: At Cardiff the annual counts and severity increased in the 1960s, declined in the 1970s and rose again in the 1980s. At Derby and London the annual counts and severity declined but at different rates. Start dates have tended to become earlier at Cardiff and Derby, but later at London. Trends in annual counts and severity are similar to changes in grassland areas but they cannot be accounted for entirely by these. Weather in spring and early summer has tended to become warmer but there are no sustained patterns in June and July. No trends are apparent in the rainfall records for these months. The maximum explanation (r2 >/= 95%) in forecast models was obtained using 10-day aggregates of weather.
CONCLUSION: The contrasting patterns both in the pollen records and land-use changes between the three sites emphasize the need for regional data. The predictive models achieved a high degree of explanation enabling pollen season severity to be forecast with high confidence shortly before the start date.

Entities:  

Mesh:

Year:  1999        PMID: 10202342     DOI: 10.1046/j.1365-2222.1999.00369.x

Source DB:  PubMed          Journal:  Clin Exp Allergy        ISSN: 0954-7894            Impact factor:   5.018


  23 in total

1.  Biometeorological and autoregressive indices for predicting olive pollen intensity.

Authors:  J Oteros; H García-Mozo; C Hervás; C Galán
Journal:  Int J Biometeorol       Date:  2012-06-02       Impact factor: 3.787

2.  A 30-day-ahead forecast model for grass pollen in north London, United Kingdom.

Authors:  Matt Smith; Jean Emberlin
Journal:  Int J Biometeorol       Date:  2006-01-04       Impact factor: 3.787

3.  Poaceae pollen in Galicia (N.W. Spain): characterisation and recent trends in atmospheric pollen season.

Authors:  V Jato; F J Rodríguez-Rajo; M C Seijo; M J Aira
Journal:  Int J Biometeorol       Date:  2009-04-04       Impact factor: 3.787

4.  Spatial and temporal modeling of daily pollen concentrations.

Authors:  Curt T Dellavalle; Elizabeth W Triche; Michelle L Bell
Journal:  Int J Biometeorol       Date:  2011-02-18       Impact factor: 3.787

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

Authors:  Nabaz Khwarahm; Jadunandan Dash; Peter M Atkinson; R M Newnham; C A Skjøth; B Adams-Groom; Eric Caulton; K Head
Journal:  Int J Biometeorol       Date:  2014-01-31       Impact factor: 3.787

6.  On the causes of variability in amounts of airborne grass pollen in Melbourne, Australia.

Authors:  Julian de Morton; John Bye; Alexandre Pezza; Edward Newbigin
Journal:  Int J Biometeorol       Date:  2010-09-04       Impact factor: 3.787

7.  Human-modified temperatures induce species changes: Joint attribution.

Authors:  Terry L Root; Dena P MacMynowski; Michael D Mastrandrea; Stephen H Schneider
Journal:  Proc Natl Acad Sci U S A       Date:  2005-05-17       Impact factor: 11.205

8.  Influence of meteorological parameters on Olea pollen concentrations in Córdoba (south-western Spain).

Authors:  L M Vázquez; C Galán; E Domínguez-Vilches
Journal:  Int J Biometeorol       Date:  2003-08-19       Impact factor: 3.787

9.  A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function.

Authors:  Kyu Rang Kim; Mijin Kim; Ho-Seong Choe; Mae Ja Han; Hye-Rim Lee; Jae-Won Oh; Baek-Jo Kim
Journal:  Int J Biometeorol       Date:  2016-07-07       Impact factor: 3.787

Review 10.  Aeroallergens, allergic disease, and climate change: impacts and adaptation.

Authors:  Colleen E Reid; Janet L Gamble
Journal:  Ecohealth       Date:  2009-09       Impact factor: 3.184

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.