A Wall of Smoke: Understanding the Drivers of the June 2023 Canadian Wildfire Smoke Event for New York City

<p>The 2023 Canadian wildfire season was notable for its extent and the dense smoke it produced. Motivated by the resulting air quality impacts in the New York City region, we explore how the total particulate matter output from the Quebec fires influenced PM2.5 concentrations downstream in New York City. Using injection height data from the Regional Advanced Baseline Imager + VIIRS Emissions dataset, we relate the tropospheric injection heights of the fires with the broader synoptic context and surface PM2.5 concentrations. Using data from the NYS Mesonet Profiler Network, radiosondes, and ERA5 reanalysis, we document the connection between an anomalous quasi-stationary low pressure system over the northeast and anomalously high surface PM2.5 values approximately 1000 km away from the fires. We conclude that a strong, stationary extratropical cyclone east of the fires organized the smoke plume and advected smoke into New York City. This smoke remained at high concentrations throughout the boundary layer and lower troposphere. Additionally, the diurnal cycle of the source fires matches the diurnal cycle of PM2.5 concentrations in New York City downstream. Trajectory analyses, observations, and reanalysis all converge on this understanding. Finally, the anomalous nature of the stationary extratropical cyclone contributed to the uniqueness of this event</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Earth and Atmospheric Sciences

Year dc:date.available

2025

Author dc:creator

• Randazzo, Gregory S

Contributors dc:contributor

• Spencer Hill
• James Booth

• Weather
• Climate
• Wildfire
• Smoke
• Hazard
• Health
• Atmospheric Sciences
• Meteorology
• Oceanography and Atmospheric Sciences and Meteorology

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/1193

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-2320