Predicting onset and duration of airborne allergenic pollen season in the United States

Abstract

Allergenic pollen is one of the main triggers of Allergic Airway Disease (AAD) affecting 5% to 30% of the population in industrialized countries. A modeling framework has been developed using correlation and collinearity analyses, simulated annealing, and stepwise regression based on nationwide observations of airborne pollen counts and climatic factors to predict the onsets and durations of allergenic pollen seasons of representative trees, weeds and grass in the contiguous United States. Main factors considered are monthly, seasonal and annual mean temperatures and accumulative precipitations, latitude, elevation, Growing Degree Day (GDD), Frost Free Day (FFD), Start Date (SD) and Season Length (SL) in the previous year. The estimated mean SD and SL for birch (Betula), oak (Quercus), ragweed (Ambrosia), mugwort (Artemisia) and grass (Poaceae) pollen season in 1994–2010 are mostly within 0 to 6 days of the corresponding observations for the majority of the National Allergy Bureau (NAB) monitoring stations across the contiguous US. The simulated spatially resolved maps for onset and duration of allergenic pollen season in the contiguous US are consistent with the long term observations.

Disentangling the effects of feedback structure and climate on Poaceae annual airborne pollen fluctuations and the possible consequences of climate change

“…However, the effects of temperature and precipitation on Poaceae development must be interpreted with caution. Recent studies such as Zhang et al (2014) and Zhang et al (2015) claim that these effects may be confused with air pollutants (e.g. ozone), carbon dioxide, other greenhouse gases, cloud coverage and sunshine hours.…”

Poaceae pollen as the leading aeroallergen worldwide: A review

“…The standardization of pollen monitoring enables comparison of data between sites, for example with respect to temporal and spatial variations in diurnal and daily concentrations, as well as seasonal characteristics (e.g., pollen‐season timing and intensity) and trends over time (e.g., in relation to land‐use and climate change). The production of comparable data also provides opportunities to construct models for predicting airborne pollen over large geographical areas …”

A prognostic pollen emissions model for climate models (PECM1.0)

“…Using local pollen count data, Y. Zhang et al (2015b) completed a regional phenological analysis using multiple linear regressions for pollen in southern California for six taxa. Olsson and Jönsson (2014) show that empirical models based solely on spring temperature perform just as well as process-based models using the temperature forcing concept, and better than those including a chilling or dormancybreaking requirement.…”