Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 773, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2021.145030
Keywords
Wildfire smoke; Ozone; PM2.5; Black carbon; Organic carbon; Optical properties
Categories
Funding
- New York State Energy Research and Development Authority (NYSERDA) [101132, 137482]
- Northeast States for Coordinated Air Use Management (NESCAUM) [2411, 2417]
- National Oceanic and Atmospheric Administration-Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies (NOAA-CESSRST) [NA16SEC4810008]
- NESCAUM
- National Fish and Wildlife Foundation
- NIST
- NOAA/ARL
- MDE
- Federal Emergency Management Agency [FEMA4085-DR-NY]
- NYS Division of Homeland Security & Emergency Services
- state of New York
- the Research Foundation for the State University of New York (SUNY)
- University at Albany, SUNY
- Atmospheric Sciences Research Center (ASRC) at SUNY Albany
- Department of Atmospheric and Environmental Sciences (DAES) at SUNY Albany
- NASA ESD Tropospheric Composition Program
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This study investigates the transport of wildfire smoke in the urban and coastal areas of New York City during the summer of 2018, revealing the optical properties of smoke particles and the significant impact of aloft smoke plumes on air quality. The findings suggest that the wildfire smoke particles have distinctive features and can contribute significantly to air pollution in populated areas like NYC.
Air pollution associated with wildfire smoke transport during the summer can significantly affect ozone (03) and particulate matter (PM) concentrations, even in heavily populated areas like New York City (NYC). Here, we use observations from aircraft, ground -based lidar, in-situ analyzers and satellite to study and assess wildfire smoke transport, vertical distribution, optical properties, and potential impact on air quality in the NYC urban and coastal areas during the summer 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We investigate an episode of dense smoke transported and mixed into the planetary boundary layer (PBL) on August 15-17, 2018. The horizontal advection of the smoke is shown to be characterized with the prevailing northwest winds in the PBL (velocity > 10 in /s) based on Doppler wind lidar measurements.The wildfire sources and smoke transport paths from the northwest US/Canada to northeast US are identified from the NOAA hazard mapping system (HMS) fires and smoke product and NOAA-HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory analysis. The smoke particles are distinguished from the urban aerosols by showing larger lidar-ratio (70-sr at 532-nm) and smaller depolarization ratio (0.02) at 1064-nm using the NASA High Altitude Lidar Observatory (HALO) airborne high -spectral resolution lidar (HSRL) measurements. The extinction related angstrom exponents in the near-infrared (IR at 1020-1640 nm) and Ultraviolet (UV at 340-440 nm) from NASA-Aerosol Robotic Network (AERONET) product show a reverse variation trend along the smoke loadings, and their absolute differences indicate strong correlation with the smoke-Aerosol Optical Depth (AOD) (R > 0.94). We show that the aloft smoke plumes can contribute as much as 60-70% to the column ADD and that concurrent high-loadings of O-3, carbon monoxide (CO), and black carbon (BC) were found in the elevated smoke layers from the University of Maryland (UMD) aircraft in-siLu observations. Meanwhile, the surface PM2.5 (PM with diameter <= 2.5 mu m), organic carbon (OC) and CO measurements show coincident and sharp increase (e.g., PM2.5 from 5 mu g/m(3) before the plume intrusion to similar to 30 mu g/m(3)) with the onset of the plume intrusions into the PBL along with hourly 03 exceedances in the NYC region. We further evaluate the NOAA-National Air Quality forecasting Capability (NAQTC) model PBL-height, PM2.5, and O-3 with the observations and demonstrate good consistency near the ground during the convective PBL period, but significant bias at other times. The aloft smoke layers are sometimes missed by the model. (C) 2021 Elsevier B.V. All rights reserved.
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