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Bayesian analysis of climate change effects on observed and projected airborne levels of birch pollen

Zhang, Yong, Isukapalli, Sastry S., Bielory, Leonard, Georgopoulos, Panos G.
Atmospheric environment 2013 v.68 pp. 64-73
Bayesian theory, Betula, atmospheric chemistry, carbon dioxide, climate change, models, pollen, spring, temperature
A Bayesian framework is presented for modeling effects of climate change on pollen indices such as annual birch pollen count, maximum daily birch pollen count, start date of birch pollen season and the date of maximum daily birch pollen count. Annual mean CO2 concentration, mean spring temperature and the corresponding pollen index of prior year were found to be statistically significant accounting for effects of climate change on four pollen indices. Results suggest that annual productions and peak values from 2020 to 2100 under different scenarios will be 1.3–8.0 and 1.1–7.3 times higher respectively than the mean values for 2000, and start and peak dates will occur around two to four weeks earlier. These results have been partly confirmed by the available historical data. As a demonstration, the emission profiles in future years were generated by incorporating the predicted pollen indices into an existing emission model.