Abstract
Data collected by a ground-based solar spectrometer at Collegeville, MN, was used to generate Aerosol Optical Depths (AODs) throughout the 2017 calendar year. The AOD data was then visualized at 13 selected wavelengths throughout the year and analyzed in comparison to satellite imagery, upper air charts and backwards trajectories of air masses moving towards Central Minnesota in order to determine key dates of interest that correspond to times before (20170615), during (20170729), and at the conclusion of (20170914) forest fires that burned in British Columbia (BC) during the summer of 2017. The data from these specific days were analyzed further by inputting the maximum and minimum AODs for each day into a Parameter Based Particle Swarm Optimization (PBPSO) algorithm in order to generate bimodal lognormal particle size distributions. The bimodal distributions were chosen because they carry more information about the aerosol loads across the entire spectrum of particle radii. The resulting distributions show an increase in number density and decrease in median radius in the Aitken mode during the BC forest fires and a relatively constant (within uncertainty) number density of accumulation mode particles at daily maximum AODs. Comparing the resulting bimodal lognormal distribution for daily minimum AODs (where evaporation and other diurnal effects are at a minimum) shows an increased number density of Aitken mode particles by two orders of magnitude from pre- to post-forest fires. This measured increase in the number density of smaller radii particles due to forest fires illustrates the PBPSO’s capability of distinguishing variations in atmospheric aerosol particle number size distributions in the Aitken mode based on data collected by the Kipp-Zonen PGS-100 solar spectrometer. KEYWORDS: Atmospheric Aerosol; Particle Swarm Optimization; Aerosol Optical Depth; Solar Spectrometer; Size Distributions; Forest Fire; Satellite Imagery; Upper Air Charts; Backward Trajectory
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