skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Search for: All records

Creators/Authors contains: "Henderson, Barron H"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national‐level alert‐day identification relies predominately on surface monitor networks and forecasters. Satellite‐based estimates of surface air quality have rapidly advanced and have the capability to inform exposure‐reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite‐based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 μg m−3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person‐alerts (alert days × population) in 2020 over polar‐orbiting satellite data. We apply pre‐existing estimates of PM2.5exposure reduction by individual behavior modification and find these additional person‐alerts may lead to 1,200 (800–1,500) or 54% more averted PM2.5‐attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8–17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions. 
    more » « less
  2. null (Ed.)
  3. We use a 0-D photochemical box model and a 3-D global chemistry-climate model, combined with observations from the NOAA Southeast Nexus (SENEX) aircraft campaign, to understand the sources and sinks of glyoxal over the Southeast United States. Box model simulations suggest a large difference in glyoxal production among three isoprene oxidation mechanisms (AM3ST, AM3B, and MCM v3.3.1). These mechanisms are then implemented into a 3-D global chemistry-climate model. Comparison with field observations shows that the average vertical profile of glyoxal is best reproduced by AM3ST with an effective reactive uptake coefficient γglyx of 2 × 10-3, and AM3B without heterogeneous loss of glyoxal. The two mechanisms lead to 0-0.8 µg m-3 secondary organic aerosol (SOA) from glyoxal in the boundary layer of the Southeast U.S. in summer. We consider this to be the lower limit for the contribution of glyoxal to SOA, as other sources of glyoxal other than isoprene are not included in our model. In addition, we find that AM3B shows better agreement on both formaldehyde and the correlation between glyoxal and formaldehyde (RGF = [GLYX]/[HCHO]), resulting from the suppression of δ-isoprene peroxy radicals (δ-ISOPO2). We also find that MCM v3.3.1 may underestimate glyoxal production from isoprene oxidation, in part due to an underestimated yield from the reaction of IEPOX peroxy radicals (IEPOXOO) with HO2. Our work highlights that the gas-phase production of glyoxal represents a large uncertainty in quantifying its contribution to SOA. 
    more » « less