AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Networks of Multi-wavelength MicroAeth Monitors Provide Tracer of Ground Level Air Pollution Impacts of Long Range Transport of Wildfire Plumes in NYC
STEVEN CHILLRUD, Qiang Yang, Beizhan Yan, Mark Arend, Fred Moshary, Jeff Blair, Yonghua Wu, Tanja Dobovicnik, Michele Markowitz, Wade McGillis, LDEO of Columbia University
Abstract Number: 728 Working Group: Biomass Combustion: Emissions, Chemistry, Air Quality, Climate, and Human Health
Abstract The frequency and intensity of wildfires is increasing, in part due to climate change. The plumes from larger wildfires can be tracked via remote sensing for thousands of miles but whether they lead to increased exposure to humans or wildlife depends upon whether and when the plume is mixed down to ground level. Routine air pollution monitoring data can show temporal increases for a wide variety of air pollutants (e.g., PM2.5, carbon monoxide, Ozone, etc) that are also impacted by other sources, making assessments reliant upon having long term data sets and having other key data (meteorological, LIDAR and ceilometer data). Here we focus on wildfire transport events that reached ground level of NYC based on remote sensing, NOAA back trajectories and local ceilometer and lidar data and compare their impact on a more specific tracer of wildfire plumes- multiwavelength microAeth (MA350s, Aethlabs) data collected at two locations- one in the highlands of Northern Manhattan and the second that captures the upwind air mass roughly 15 miles north of the other site. Additional data not already mentioned above are also available for these events, including: NOAA products for backward trajectories and HRRH column smoke for the wild fire plumes showing it reaching NYC and NYS, and data collected from microAeths in the San Francisco area confirm the ultraviolet (UV) signature of the wildfire event. The multi-wavelength microAeth data clearly shows a strong wildfire signal based on the strong UV signature of wildfire emissions whose strength is well timed with the LIDAR and Ceilometer data and of equal magnitude at the two sites described above consistent with an upwind source far away; in comparison due to local emissions, the black carbon data at the urban site is much higher than that at the upwind site.