Airborne pollen can impact human health by causing seasonal allergies and contribute to the total amount of particulate matter in the atmosphere. Current observations of pollen are limited in both space and time, making it is difficult to accurately forecast how pollen is released into the environment. Lidar is a ground-based remote sensing technique that can identify particles in the atmosphere, and depolarized light can identify irregularly shaped particles like pollen. We deployed a ground-based lidar with depolarization at a forested site in northern Michigan during the spring tree pollination season to understand the timing and contribution of pollen to the total amount of particulate matter in the atmosphere. We identify nine pollen events at the forested site that lead to high particulate matter in the atmosphere. This dataset includes the processed lidar data using the MiniMPL raw event count , which is calibrated and normalized to calculate the normalized relative backscatter (NRB) as a function of height (Ware et al., 2016).
