Current predictions of pollen levels rely strictly on historical Averages, regardless of environmental factors that might affect the timing of pollen release by different plants. For this thesis, the goal was to develop a statistical model that will accurately forecast pollen levels by correlating those daily counts to atmospheric and meteorological conditions. This project used ARIMA modeling on IBM’s SPSS Statistics 24 of daily pollen count information for multiple allergenic pollens in the Sarasota County, Florida area over a 11-year period. The pollen species in question for this project are oak and cypress trees, grass, and ragweed pollens; and Alternaria and Cladosporium mold spores. The total pollen counts for weeds, grass, trees, and overall total are also included in the 11 years of data. The atmospheric variables used to predict pollen levels are high temperature, low temperature, average temperature, precipitation, humidity, wind direction, and wind speed for daily observations over the 11-year period. Results for these models showed that maximum temperature, precipitation, humidity, and wind direction were the driving predictors behind the pollen counts in Sarasota, Florida. The analysis of the pollination periods also showed that there were phenological changes according to the specific species. The models and phenological changes are specific to the Sarasota, Florida area, and would serve as a framework for studying other pollination regions.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-8042 |
| Date | 24 June 2017 |
| Creators | Gessman, Daniel J. |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
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